iii - Aaltodoc

iii

ACKNOWLEDGEMENTS

The research presented in this dissertation was carried out at the Mechanical

Process and Recycling Laboratory, Department of Materials Science and Rock

Engineering, Helsinki University of Technology (HUT). Concrete castings were

performed at the concrete laboratory of Fortum Technologies in Vantaa and the statistical

modelling was carried out at the Laboratory of Computational Engineering, Department

of Electrical and Communications Engineering, Helsinki University of Technology. The

work was financed by Lohja Rudus Oy Ab, and the initiation for the dissertation was

introduced by the Scancem Scientific Counsel.

I would profoundly like to thank Professor Kari Heiskanen, under whose

supervision this study was carried out. The technical support was never compromised

whenever that was needed.

I also wish to thank Professor Göran Fagerlund and Dr Ernst M∅ rtsell for

reviewing the dissertation and for their comments and suggestions on the text.

Additionally, I want to extend my gratitude to Professor Jouko Lampinen for reviewing

the statistical approach of the dissertation.

The support of my employer, Lohja Rudus Oy Ab is gratefully acknowledged and

especially the encouragement received from M.Sc. Martti Kärkkäinen and M.Sc. Kauko

Linna is highly appreciated.

I want to express my sincere gratitude to M.Sc. Ville Toivanen for diligent and

intelligent assisting work, Mr. Tuomo Rimpiläinen and his group at Fortum for

accurately performed concrete castings and M.Sc. Aki Vehtari for the demanding

statistical modelling. Also the work of Mr. Ilkka Kalliomäki for the statistical program is

gratefully appreciated. It has been truly enjoyable and easy to work with all of you.

Many thanks are due to my colleagues at Lohja Rudus Oy Ab and the Laboratory

of Mechanical Process and Recycle for their support and interest as well as for

interdisciplinary and witty discussions.

Special thanks are due to my friends close by and abroad for balancing the life.

Finally, I wish to thank my parents for everything.

Hanna Järvenpää

iv

CONTENTS

Abstract ii

Acknowledgements iii

Contents iv

Definitions and notations xi

1. Introduction 1

2. Effects of aggregate characteristics on concrete properties 42.1 Workability 4

2.1.1 Effect of paste and water content 42.1.2 Effect of aggregate grading, surface area and size 52.1.3 Effect of aggregate shape, angularity and surface texture 62.1.4 Effect of aggregate mineralogy 72.1.5 Effect of aggregate absorption 82.1.6 Effect of superplasticizer and air-entraining agent 10

2.2 Air percentage 112.2.1 Air-void formation and stability 112.2.2 Effect of water-cement ratio 122.2.3 Effect of aggregate grading 122.2.4 Effect of aggregate shape, angularity and surface texture 132.2.5 Effect of aggregate mineralogy 142.2.6 Effect of superplasticizer 14

2.3 Bleeding 142.3.1 Definition of stability, viscosity and cohesion 142.3.2 Effect of cement and workability 152.3.3 Effect of aggregate surface area, grading and size 152.3.4 Effect of aggregate shape, angularity and surface texture 162.3.5 Effect of superplasticizer and air-entraining agent 17

v

2.4 Compressive strength 172.4.1 Effect of water-cement ratio and

aggregate-paste interface 172.4.2 Effect of compaction degree 192.4.3 Effect of aggregate size 192.4.4 Effect of aggregate strength 202.4.5 Effect of aggregate shape, angularity and surface texture 202.4.6 Effect of aggregate surface area 222.4.7 Effect of aggregate mineralogy 222.4.8 Effect of superplasticizer and air-entraining agent 23

2.5 Drying shrinkage 242.5.1 Mechanism of drying shrinkage 242.5.2 Effect of water-cement ratio 242.5.3 Effect of aggregate content 252.5.4 Effect of elastic modulus of aggregate 252.5.5 Effect of aggregate grading, shape, size, angularity

and surface texture 252.5.6 Effect of aggregate shrinkage properties 262.5.7 Effect of superplasticizer and air-entraining agent 27

3. Data analysis – methods and Excel –program used 283.1 Inputs – outputs 283.2 Bayesian statistics and Gaussian processes for prediction of

the fine aggregate-concrete interaction 293.2.1 Bayesian methods 293.2.2 Gaussian Process 293.2.3 Relevance values of inputs 323.2.4 Deviance Information Criterion (DIC) for model evaluation 333.2.5 Data pre-processing 343.2.6 Model selection 343.2.7 Model errors (prediction errors) 35

vi

3.3 Excel program for prediction of fine aggregate – concrete interaction 353.3.1 General principles of the Excel program 353.3.2 Predicting the correlation in input variables 363.3.3 Sensitivity analysis and its reliability 37

3.4 Error estimation 39

4. Experimental programme 404.1 Materials 40

4.1.1 Aggregate products 404.1.2 Cement 414.1.3 Admixtures 42

4.2 Test programme 434.2.1 Aggregate (inputs) 434.2.2 Concrete (outputs) 44

4.3 Mix designs and concrete mixes, mixing procedure, test specimens 464.3.1 Mix designs and concrete mixes 464.3.2 Mixing procedure 494.3.3 Test specimens 50

4.4 Testing methods and potential input values, aggregates 514.4.1 Mineralogical composition, fines and semi-coarse fractions 514.4.2 Specific surface area, fines 514.4.3 Grading, fines 554.4.4 Particle density, fines and semi-coarse factions 554.4.5 Particle porosity, fines and semi-coarse fractions 564.4.6 Zeta potential, fines 574.4.7 Resistance to fragmentation, semi-coarse fractions 584.4.8 Elongation, flakiness, particle volume and quantity,

semi-coarse fractions 584.4.9 Angularity/roundness, semi-coarse fractions 604.4.10 Surface texture, semi-coarse fractions 61

vii

4.5 Testing methods and concrete output values 624.5.1 Workability 624.5.2 Air % and density of fresh concrete 634.5.3 Bleeding 644.5.4 Compressive strength and density of the hardened concrete 64

4.6 Testing methods for drying shrinkage, weight loss and air-parameters,hardened concrete 664.6.1 Drying shrinkage and weight loss 664.6.2 Thin section analysis; air %, specific surface area and

spacing factor of air void 67

5 Aggregate test results and discussion 685.1 Mineralogical composition 695.2 Grading 705.3 Specific surface area 745.4 Particle density 765.5 Particle porosity 785.6 Zeta potential 835.7 Resistance to fragmentation 855.8 Elongation, flakiness, particle volume and quantity 865.9 Angularity and surface texture 905.10 Discussion of the test results for aggregates 93

6. Concrete test results and discussion 966.1 Workability 966.2 Air % 101

6.2.1 Air %, fresh concrete 1016.2.2 Air %, hardened concrete 105

6.3 Bleeding 1076.4 Compressive strength 1106.5 Drying shrinkage and weight change 114

6.5.1 Results 1146.5.2 Discussion 118

viii

7. Models for the fine aggregate – concrete interaction 1207.1 Model for the flow value 120

7.1.1 Sensitivity analysis – flow value 1227.1.1.1 Reliability of the sensitivity analysis – flow value 1227.1.1.2 Flow value – SC-flakiness 3.15/4.0 mm, SC-angularity and

SC-elongation 3.15/4.0 mm 1227.1.1.3 Flow value – SC-pore area 300-900Å and

SC- pore area >900Å 1247.1.1.4 Flow value – F- mica %, F- Cu, F- BET value and

F-zeta potential 125

7.2 Model for air %, fresh concrete 1287.2.1 Sensitivity analysis – air % 129

7.2.1.1 Reliability of the sensitivity analysis – air % 1297.2.1.2 Air % - SC-pore area 60-300Å and SC-pore area 300-900Å 1307.2.1.3 Air % - SC- flakiness 3.15/4.0 mm and SC-angularity 1317.2.1.4 Air % - F- Cu and F- BET value 133

7.3 Model for the bleeding 1347.3.1 Sensitivity analysis – bleeding 135

7.3.1.1 Reliability of the sensitivity analysis – bleeding 1357.3.1.2 Bleeding – SC- total pore area and SC- average pore area 1367.3.1.3 Bleeding – SC- elongation 0.8/1.0 mm,

SC- flakiness 1.6/2.0 mm and SC- elongation 1.6/2.0 mm 1377.3.1.4 Bleeding – F- BET value, F- zeta potential,

F- density and Cu 139

7.4 Model for the compressive strength 1427.4.1 Sensitivity analysis – compressive strength 144

7.4.1.1 Reliability of the sensitivity analysis –compressive strength 144

7.4.1.2 Compressive strength – SC- flakiness 3.15/4.0 mm andSC- quantity 1.6/2.0 mm 144

7.4.1.3 Compressive strength – SC- Los Angeles value 1457.4.1.4 Compressive strength – SC- pore area 60-300Å 145

7.5 Discussion of the models 147

ix

8. Predicting with the models 1508.1 Principles of the predictions 150

8.1.1 Combined effect of two input characteristics 1508.1.2 Predictions with different solutions for concrete

aggregate combination 151

8.2 Predicting with the flow value model 1538.2.1 SC- pore area 300-900Å and SC- flakiness 3.15/4.0 mm

vs. flow value 1538.2.2 F- Cu and SC- flakiness 3.15/4.0 mm vs. flow value 1568.2.3 Effect of different aggregate combinations on the flow value 159

8.3 Predicting with the air % model 1608.3.1 SC- pore area 60-300Å and SC- flakiness 3.15/4.0 mm vs. air % 1608.3.2 SC- pore area 60-300Å and F- Cu vs. air % 1618.3.3 Effect of different aggregate combinations on the air % 163

8.4 Predicting with the bleeding –model 1648.4.1 SC- total pore area and F- BET value vs. bleeding 1648.4.2 SC- total pore area and SC- elongation 0.8/1.0 mm vs. bleeding 1658.4.3 Effect of different aggregate combinations on the bleeding 167

8.5 Predicting with the compressive strength model 1688.5.1 SC- Los Angeles value and SC- flakiness 3.15/4.0 mm vs.

compressive strength 1688.5.2 SC- Los Angeles value and SC- pore area 60-300Å vs.

compressive strength 1718.5.3 Effect of different aggregate combinations on

the compressive strength 174

8.6 Discussion of the predictions made with the models 175

9. Verification of the models with two new aggregtate products 1799.1 Procedure 1799.2 Identification of the new aggregate products 1799.3 Results of the modelled and measured values 1809.4 Evaluation of the verification of the modelled and measured values 183

x

10. Conclusion 184

11. Need for future research 188

REFERENCES 189

xi

DEFINITIONS AND NOTATIONS

Aggregate

Granular material used in construction. Aggregate may be natural, manufactured or

recycled.

Natural aggregate

Aggregate from mineral sources which has been subjected to nothing more than

mechanical processing.

Natural fine aggregate

Designation given to smaller aggregate sizes with upper nominal size less than or equal

to 4 mm. Fine aggregate can be produced from natural disintegration of rock or gravel

and/or by the crushing of rock or gravel.

Fines

Particle size fraction of an aggregate, which passes the 0.063 mm sieve (aggregate

testing) or the 0.125 mm sieve (concrete castings).

AE air-entrainment (concrete)

ARD Automatic Relevance Determination

DIC Deviance Information Criterion

F fines (< 0.063 mm or 0.125 mm see above)

FG future gravel

GP Gaussian Process

gSH good shape

gST good strength (Los Angeles value)

MCMC Markov Chain Monte Carlo

N no admixture (concrete)

PG past gravel

pSH poor shape

pST poor strength (Los Angeles value)

SC semi-coarse

SCF semi-coarse fraction (0.125 – 4 mm)

WR superplasticizer (concrete)

1

1. Introduction

For the aggregate producer, the concrete aggregates are end products, while, for the

concrete manufacturer, the aggregates are raw materials to be used for mix designs and

successful concrete production. With the aggregate production, the quality of the

aggregate products can be influenced, but the raw material – the gravel or rock - may

have characteristics which cannot be modified by the production process. Similarly, with

the concrete mix designs can be influenced how the aggregate affects the properties of

concrete, but there is also a limit, whether technical and/or economic, in the mix design

modification after which it is useful to select a more suitable aggregate product.

Concrete aggregates have been studied relative largely in the past decades, though most

of the research has been done to the coarse aggregate and only to one or few quality

characteristics at a time. In order to optimise the aggregate-concrete chain, one has to

know what are the aggregate quality characteristics that dominate different concrete

properties, and how basic changes in the concrete mix design affect the influences. The

need for knowledge is increasing as conventional concrete aggregate supplies are

becoming depleted, and environmental aspects prevent the use of existing sources.

The objectives of this work are:

1. To determine which the most important fine aggregate characteristics are that

affect the concrete workability, compressive strength, air %, bleeding and drying

shrinkage.

2. To determine how the aggregate characteristics affect the concrete properties

separately and together.

3. To determine how basic changes in the concrete mix design, i.e. change in paste

amount and admixtures (air-entraining agent, superplasticizer) affect the

aggregate influences.

4. To become a program with which can be predicted how the fine aggregate –

concrete interaction affects the concrete workability, compressive strength, air %

and bleeding.

2

When the objectives are fulfilled, the results are applicable for optimising both the

aggregate and concrete production.

The experimental studies were divided to aggregate tests for aggregate characteristics

determination and to concrete tests where the tested aggregates were used for concrete

production and observation of behaviour in concrete properties. To be able to relate the

extent that the aggregate has on the concrete as compared against the effect of the

changes in mix design, the test programme was built to monitor the aggregate behaviour

in different mix designs including variations in admixtures and paste amount. The test

programme was additionally constructed in such a way that the effect of the fines and the

semi-coarse fraction could be interpreted separately. The fines are defined in this study as

the fractions bellow 125 µm and the semi-coarse fractions as particle sizes between 125

µm and 4 mm. The aggregate characteristics and basic concrete mix design changes are

regarded as input variables and the fresh and hardened concrete properties are the

outputs to be modelled.

Due to the restricted amount of data and lack of knowledge concerning which aggregate

characteristics are relevant and their relationships to the concrete properties, it was

decided to apply the Bayesian statistics and non-parametric non-linear Gaussian process

models. The Bayesian statistics is based on learning processes where the prior

information is combined with the evidence from the data. The results are treated as

probability distributions expressing our beliefs regarding how likely the different

predictions are. In a non-parametric model, the aggregate characteristics – concrete

properties relationship is determined from the data without reference to an explicitly

parameterised model and thus, the possible different behaviour of aggregate inputs in the

different mix design conditions can be concurred in the model. The adoption of a non-

linear model enables the possibility of non-linear conduct of the aggregate

characteristics.

The statistical modelling was performed by the laboratory of computational engineering

at the Technical University of Helsinki.

3

The results of the models in this study are presented in the following formats:

1. ARD (Automatic Relevance Determination) listing gives the relevance values of the

inputs, i.e. aggregate characteristics and basic mix design changes, in each model.

The relevance value determines the distance of each input in the space of the model

over which the input is expected to vary significantly.

2. Sensitivity analysis figures present how the output changes when an input variable is

changed by a small amount. The predicted output is calculated and by comparing the

change made in the input variable to the change perceived in the output, we see how

the model reacts to changes in that particular input variable. The sensitivity analysis

figures are presented for all inputs in each of the models except for SEM/N/AE/WR

which are of nature “on/off”.

3. Sensitivity reliability figures present the difference between the modelled and

measured values and thus, indicate how reliable the model is in general and on

specific input-output-mix design combination.

4. Example predictions of combined effect of two aggregate input characteristics

present how the models can be used for predictions for the combined effect on one

output of two input characteristics over their total variation range. The result is

shown by means of 3D surface charts and in the calculations the other characteristics

of the model have fixed values. The charts are drawn for each mix design separately,

as the mix design parameters (SEM, N/AE/WR) are additional influencing

characteristics for the output. The example predictions are executed to one possible

future solution for concrete aggregate combination.

5. Example predictions with different solutions for concrete aggregate combination

present how the models can be used for predicting the effect of different aggregate

products on the concrete properties. The results are shown in column charts and for

each mix design separately, as the mix design parameters (SEM(N/AE/WR) are

additional influencing characteristics for the outputs. In these predictions all the input

characteristics are changed according to the aggregate combination. The example

predictions are executed to three different solutions for concrete aggregate

combinations; past and future gravel and combination of filler aggregate and crushed

rock including predictions for the variation on shape and strength characteristics.

6. Verification of the models with two new aggregate products.

4

2 EFFECTS OF AGGREGTE CHARCTERISTICS ON CONCRETE

POPERTIES

2.1 Workability

2.1.1 Effect of paste and water content

Any mix made with given materials and having a certain consistency will have a point at

which the water ratio or the voids ratio is at a minimum. This point of maximum solids

content also determines the optimum paste consistency. POWERS (1968) has defined the

optimum paste consistency as follows: “Optimum paste consistency is that consistency at

which the solids content of the paste and the paste content of the mix are such that they

produce the maximum solids content possible with the given materials.”. Several

commercial programs for particle packing prediction are now available, e.g. LPDM

(Linear Packing Density Model) and Europack.

For the concrete mix to be plastic, the volume of the cement paste must be sufficient to

fill the interstitial space of the compacted aggregate, plus an increment that causes a

certain dispersion of the aggregate particles (figure 1). (POWERS 1968, KRONLÖF 1997)

Figure 1. Dry compacted state of the aggregate skeleton (A)

Aggregate particles dispersed in cement paste (B) (POWERS 1968)

5

In plastic mixes, it is not paste alone that causes the dispersion of aggregate particles and

plasticity; the volume of the paste is always augmented by a certain amount of air.

According to the excess paste theory, the consistency of concrete depends on two factors:

the volume of cement paste in excess of the amount required to fill the voids of the

compacted aggregate; and the consistency of the paste itself. If the aggregate/cement

ratio or the cement content of the mix for given materials is kept constant, the

workability is then governed by the water amount. (POWERS 1968)

MØRTSELL (1996) proposed functions for workability prediction of mortars and concrete.

The inputs of the functions are the flow resistance ratio ( Qλ ) which characterises the

matrix phase and the air voids space modulus ( mH ) which characterises the aggregate

phase. For a given matrix and aggregate phase, the volume relations between the phases

determine the workability of the mortar or concrete. The matrix phase consists of water,

cement, pozzolanes, admixtures and filler (<0.125 mm aggregate).

2.1.2 Effect of aggregate grading, surface area and size

When the grading is changed for given materials, then the surface area of the aggregate

combination is also affected. The greater the exposed surface area is the more water and

cement paste will be required to wet that area and, therefore, the less water and paste will

remain to lubricate the mix and thus the lower its workability will be. Special mix

proportioning methods based on the specific surface of the aggregate combination have

been proposed by, e.g. SINGH (1959). In his method the specific surface is determined by

the water permeability method or an estimation is made on the basis of a shape factor.

The specific surface of a combination of spheres of fine aggregate, fS , is given by:

��

��

� +++++=32168421100

654321 ppppppSS f Equation 1

where

6

S = the specific surface of the smallest size group,

i.e. No. 100/ No. 200 sieves (0.075/0.150 mm)

61...pp = percentage weight of six groups in the fine aggregate,

from the smallest to the largest (0.075-0.150-0.300-0.600-1.2-2.4-4.8 mm)

LOUDON (1952-53) studied the shapes of different fine aggregates and he arrived at a

determination of angularity factor, f, where the angularity is expressed as the ratio of the

specific surface of a size group to the specific surface of spheres of a corresponding size

group. He suggested that f = 1.1 for a rounded fine aggregate, f = 1.25 for a fine

aggregate of medium angularity, and f = 1.4 for an angular fine aggregate. The specific

surface is thus shape corrected by multiplying the specific surface of spheres by an

appropriate angularity factor.

MURDOCK (1960) suggested a modified method that also takes the maximum particle size

into account. In addition to having a relatively smaller surface area requiring wetting an

increased maximum aggregate size also presents the possibility of denser packing.

With a given quantity of paste, decreasing the percentage of fine aggregate decreases the

surface area, and hence the surface tension, thus tending to increase the mobility of the

mix. In lean mixes (those with a low amount of cement) and gap graded mixes, the

percentage of fine aggregate should, however, be high enough to ensure sufficient

cohesion. Concrete mixes of which good mobility is required should also have an

adequate surface area enabling good cohesion and shear resistance (see 2.2).

2.1.3 Effect of aggregate shape, angularity and surface texture

As was discussed earlier, the shape factors influence the specific surface; aggregates

which are flaky, elongated and/or angular thus require more paste to wet the surfaces.

The shape and texture of the aggregate also affect the bulk density of the aggregate

skeleton and for rough, poor-shaped aggregate, the bulk density is therefore less than that

of smooth, well-rounded particles of the same density owing to particle friction and

7

interference. Filling the voids and overcoming the friction call for a higher content of

fine aggregate and water. A higher cement amount may also be necessary, to keep the

strength constant.

When a mix becomes richer, the angularity and grading of the aggregates become less

important until, with high proportions of cement, the aggregate particles are little other

than “plums” floating in cement paste. The test results obtained by MURDOCK (1960)

show that, when the aggregate/cement ratio is reduced to 2, the effects of angularity and

grading become negligible.

KAPLAN (1958) studied 13 different coarse aggregates and came to the conclusion that

increased angularity and/or flakiness lead to a reduction in the workability of concrete.

Changes in the angularity, however, have a greater effect on the workability of concrete

than changes in the flakiness. In Kaplan’s studies flakiness caused 20% of the variation

in the workability, whilst 59% was due to angularity. Although in his study there was a

wide variation in the surface texture of the aggregates, Kaplan did not find any

correlation between aggregate surface texture and the workability of concrete.

In his research WILLIS (1967 studied nine different fine and coarse aggregates. He found

that an equal change in shape characteristics caused fine aggregate to need two to three

times more water than coarse aggregates. He also noted that the shape characteristics

described the concrete behaviour best, whereas the mortar tests also included the effects

of clay, mica and other deleterious materials. Owing to the method that was used to

determine the shape (flow rate through an orifice), we can conclude that the findings

reported by Willis are actually caused by a combination of shape, angularity and surface

texture effects.

2.1.4 Effect of aggregate mineralogy

Clay minerals are normally sheet-shaped i.e. they have more surface area than other

minerals of the same grain size. The ratio of thickness to length for clay particles is

8

normally near 20. This makes the surface area of a clay particle nearly three times that of

a cube of the same volume (non-expanding clays).

Clays normally have a charged surface, and thus they attract charged ions and/or water

molecules to adsorb on the surface. With some clays, the activity of the surface is

increased by a sort of internal surface into which charged ions and water molecules can

find their way (expanding clays). These absorbed ions and molecules expand the clay,

and the surface area can be increased by a factor of 25 or more. (VELDE 1995)

DANIELSEN AND RUESLÅTTEN (1984) studied micas in the size range of 0.15/0.30 mm.

They found that micas have a negative effect on the workability properties of concrete.

The effect is even greater for muscovite than for biotite, but only in the case of newly

crushed, unweathered micas. For gravel-based, weathered micas, there is no difference

in behaviour between muscovite and biotite.

Particle degradation during mixing (flaky and elongated or otherwise mechanically weak

particles) may cause an increased water requirement, slump loss, and a reduced air

content of the air-entrained concrete. Additionally, if the aggregate particles have a

coating which is soft or loosely adherent, the coating may be removed during the mixing

and this would increase the fines amount of the grading.

2.1.5 Effect of aggregate absorption

The mix design procedure now prevailing in Finland is based on the total water/cement

ratio, i.e. the aggregate is considered to be in the bone-dry state. The mix design

procedure most commonly applied in other countries is based on the effective

water/cement ratio, which excludes the water absorbed by the aggregates (figure 2). This

is also the case with EN 206-1, “Concrete – Part 1: Specification, performance,

production and conformity”.

9

Figure 2. Different aggregate moisture conditions (NEVILLE 1995)

The effective water/cement ratio and the free water content are difficult to determine. For

both coarse and fine aggregates, the absorption of bone-dry aggregate to the state of

saturated and surface dry (SSD) is determined with standard tests that are hence accurate,

though both methods have their own reproducibility and repeatability errors. Normally, it

is assumed that, at the time of the setting of concrete, the aggregate is in an SSD

condition. When aggregates are dry, e.g. in spring and summer the particles may quickly

get coated with cement paste which prevents the further ingress of water necessary for

saturation; in consequence, the effective water/cement ratio is higher than assumed. On

the other hand, when the water/cement ratio is calculated on the bone-dry basis, the

effective water content is always less than calculated recipe water. In this case, too, the

effective water content varies according to the prevailing moisture content of the

aggregate products and mix designs, as in richer mixes the coating effect of the cement

tends to be quicker than in leaner mixes. (SINGH 1958, NEVILLE 1995)

Most of the water absorption occurs by the outer layer of the aggregate particles. Some

aggregates, especially gravel products, can have a weathered “patina” outer layer. The

minerals of the outer surface can be altered and/or some minerals may have been leached

away, causing enhanced porosity. The weathered gravel with a “patina” outer layer

absorbs more than crushed product produced from the same raw material. This is due to

the fresh, less porous unweathered surfaces, which appear during crushing. (KAPLAN

1958)

10

2.1.6 Effect of superplasticizer and air-entraining agent

When an air-entraining admixture is used the water content and/or the share of fine

aggregate can be reduced. An 1 per cent increase in air is equivalent to a 1 per cent

increase in fine aggregate or a 3 per cent increase in the unit water content (ACI

COMMITTEE 309, 1981). The reason for the improved workability brought about by the

entrained air is that the air bubbles, kept spherical by surface tension, act as a fine

aggregate having a very low surface friction and considerable elasticity. Figure 3 shows

the indicative reduction of the water content as a function of the percentage of added air

and the cement content.

Figure 3. Reduction in the mixing water due to entrained air (NRCA 1993)

For very lean mixes with an aggregate/cement ratio of 8 or more, and particularly when

an angular aggregate is used, the improvement in workability caused by air entrainment

is such that the resultant decrease in the water/cement ratio compensates fully for the loss

of strength resulting from the presence of the voids. (POWERS 1968)

Superplasticizers adsorb onto the surface of cement and aggregate particles and alter the

electrical charge of the surface and/or cause physical interference (steric repulsion)

between particles. The deflocculation and dispersion of cement and aggregate fines is

thus enhanced and the workability is increased.

11

2.2 Air percentage

2.2.1 Air-void formation and stability

Entrapped air voids are unintentional voids. They are characteristically 1000 microns or

more in diameter and, because the periphery of the voids follows the contour of the

surrounding aggregate particles, they are usually irregular in shape. Entrained air voids,

in the contrast, are spherical or nearly so, owing to the hydrostatic pressure to which they

are subjected by the surrounding paste of water, cement and aggregate fines. These voids

are typically between 10 and 1000 microns in diameter. (MIELENZ ET AL. 1958)

Air-entraining agents adsorb at air-water interfaces, and thus the air voids that are formed

during mixing become stabilised as they are covered by a sheath of air-entraining

molecules that repel one another. Repellence prevents the coalescence of voids and

ensures uniform dispersion of the entrained air. The soluble air-entraining agents will

also precipitate on the surface of cement and aggregate particles, and will reduce the

hydrophilic quality of the surface and render it hydrophobic. Air voids tend to cling to

the hydrophobic surface of the particles. It is thus anticipated that void-particle adhesion

is most significant for certain ranges of particle size. Studies of ore flotation indicate that

particles between about 10 and 50 microns in sizes are most susceptible to void adhesion.

(NEVILLE 1995, MIELENZ ET AL. 1958)

Often there is a discrepancy between the air content measured in fresh concrete and air

content determined in hardened concrete. Three mechanisms have been proposed for air-

void instability in fresh concrete (FAGERLUND 1990):

1. Loss of coarse air voids due to handling and compaction as the large bubbles

move upwards by buoyancy

2. Dissolution of small bubbles in water as the bubbles collapse due to pressure

caused by surface tension

3. Transfer of air from small bubbles to coarse bubbles as small bubbles coalescence

with larger bubbles

12

2.2.2 Effect of water-cement ratio

The amount of entrained air is smaller with lower water/cement ratios, i.e. with higher

cement concentrations. WHITING (1985) has reported that dosages as much as ten times

greater are needed for 6.5 ±1.0% entrainment for concrete with a w/c of 0.30..0.32 (SSD)

and a maximum aggregate size of 25 mm as compared against dosages used in

conventional concrete mixes. The same phenomenon can be seen in figure 4, the air

entrainment in cement paste where is presented for different w/c ratios and air-

entrainment agent dosages. (POWERS 1968)

Figure 4. Air entrainment in cement paste as influenced by the w/c ratio and the

dosage of air-entraining agent (POWERS 1968)

2.2.3 Effect of aggregate grading

The air content of concrete increases if the proportion of intermediate size (150 – 600

µm) of fine aggregate is increased. The maximum size of the space subtended by

intermediate particles varies from about 30 to 130 microns. The size range is suitable for

enmeshing air-entrained bubbles that are big enough to withstand rapid dissolution in the

mixing water. An increase in the finer sizes of aggregate or cement beyond the optimum

13

intermediate size decreases the air content because the available volume among the

particles is decreased and hence the air bubbles become smaller. The smaller bubbles are

subjected to greater pressure than bigger bubbles, thus increasing the dissolution of the

bubbles. Further, an increase in coarse aggregate size decreases the available interstitial

space of optimum dimensions and thus decreases the air content in concrete. (MIELENZ

ET AL. 1958, SINGH 1959, PIGEON AND PLEAU 1995)


When the shape and/or angularity of the aggregate particles deviates from sphericity, the

interstitial space between the particles decreases if the most compact arrangement of the

particles is achieved. If good workability is required, the paste content of the mix design

is increased, which enlarges the interstitial space and leads to successful air entrainment

(NICHOLS JR. 1982, MIELENZ 1958). On the basis of his tests, SINGH (1959) concluded

that angular particles derive great benefit for purposeful air entrainment as they resist

compaction and thus increase the interstitial space between particles.

BACKSTROM ET AL. (1958) studied eleven aggregates, including aggregates with smooth

and rough surfaces. They found that surface texture had a to be rather striking effect on

the values of the specific surface and spacing factor in air-entrained castings. The

average value of the surface area was 742 in-1 for the smooth aggregates and 1037 in-1 for

the rough aggregates. The average values of the spacing factor were 0.0065 and 0.0045

in., respectively. They found a fairly good correlation between the spacing factor and the

freezing and thawing resistance with seven out of the eleven aggregates they tested. Four

concrete castings expanded more than would have been expected according to the

spacing factor. These aggregates all had smooth surfaces, and in petrographic

examination they were found to contain appreciable amounts of weathered and physically

unsound materials.

14


The effect of aggregates on the air-entrainment agent function varies according to the

chemical and mineralogic compositions and degrees of alteration (see 2.5.4). Aggregates

composed with alkali earth and metallic ions, e.g. limestone, dolomite, blast furnace

slags and glassy basalts, are expected to have the most considerable effect on the

performance of the air-entraining agent. (MIELENZ 1958)

2.2.6 Effect of superplasticizer

If superplasticizers are used to increase the workability of concrete, the air content of air-

entrained concrete generally increases if the other mix design parameters are constant. In

some cases, however, the air is not stable, i.e. the air-void system created during the

concrete manufacturing changes before the concrete is hardened. This has been explained

by two phenomena (PLANTE ET AL. 1989):

1. superplasticizers can entrain large bubbles, which are thus easily lost during

handling and compaction

2. superplasticizers increase the paste fluidity, thereby promoting the coalescence of

air-voids.

2.3 Bleeding

2.3.1 Definition of stability, viscosity and cohesion

Stability is defined as the flow of fresh concrete without applied force and is measured by

bleeding and segregation characteristics. Bleeding occurs when the mortar is unstable

and releases free water. Normal bleeding, which occurs in the form of uniform seepage,

is not necessarily undesirable. It is, e.g. good preventive curing against plastic shrinkage

cracking. Segregation is defined as the instability of a mix, caused by a weak matrix that

15

cannot retain individual aggregate particles in a homogeneous dispersion. Segregation is

possible in the case of both wet and dry consistencies.

Viscosity is defined as the quotient of shear stress divided by the rate of shear in a steady

flow. The viscosity of the matrix can also be said to contribute to the ease with which the

aggregate particles can move and rearrange themselves within the mix.

Cohesion is defined as the force of adhesion between the matrix and the aggregate

particles. It provides the tensile strength of fresh concrete that resists segregation.

Internal friction occurs when a mix is displaced and the aggregate particles translate and

rotate. (ACI COMMITTEE 309, 1981)

2.3.2 Effect of cement and workability

The fineness and the amount of cement greatly affect the bleeding tendency of concrete.

Finer cement decreases this tendency owing to its larger surface area, earlier hydration

and lower sedimentation rate. In addition, less bleeding occurs when cement has a high

alkali and C3A content. (NEVILLE, 1995)

A water content above that needed to achieve a workable mix produces greater fluidity

and decreased friction. Additionally, the water-cement ratio increases; this reduces the

cohesion within the mix and hence increases the potential for segregation and excessive

bleeding. An overly dry mix may also result in loss of cohesion and dry segregation. (ACI

COMMITTEE 309, 1981)

2.3.3 Effect of aggregate surface area, grading and size

The amount and surface area of the fine aggregate, especially that smaller than 150 µm,

influences the bleeding of the concrete. The increased bleeding caused by the angularity

of the fine aggregate can be controlled by the surface area. The bleeding tendency is

16

reduced by using a finer fine aggregate or by adding separate fines to the mix. The fines

automatically contained in the crushed fine aggregate as a result of the crushing

phenomenon is also suitable, though care should be taken that the amount is not too

much.

Mixes with gap grading normally require less water to achieve good workability than

continuous grading with an otherwise similar recipe. Gap grading reduces the sizes of

coarse fine aggregate and small coarse aggregate, and the tendency for bleeding and even

segregation is enhanced if the concrete has a high workability without enough cohesion

(cement, fines or air %). Additionally, if the fine aggregate fraction becomes coarse, the

cohesion is reduced thus making the mix harsh and the tendency for bleeding increases.

In contrast, as the fine aggregate becomes finer, the water requirement increases and the

concrete mix becomes increasingly sticky.

If the coarse aggregate has a large maximum size and if, in addition, the particles are

flaky an excessively workable concrete should be avoided because pockets of bleed

water may collect on the undersize of the coarse aggregate particles.


Flakiness, elongation, angularity and surface texture of the aggregate, especially with the

fine aggregate, all reduce the workability of concrete. The viscosity of the paste increases

if only water is added, and if the surface area of the paste (cement, additives, fines and

air) is too low, the extra water can overcome the cohesion and vigorous bleeding or even

segregation can occur.

17


Superplasticizers generally reduce bleeding except if there is a very high slump when the

concrete can become unstable and heavy bleeding or even segregation can occur.

(NEVILLE 1995)

Air entrainment also reduces bleeding. The reduction is caused by the displacement of

the paste, the buoyancy of the bubbles and their surface area. (POWERS 1968)

2.4 Compressive strength

2.4.1 Effect of the water-cement ratio and aggregate-paste interface

Concrete is a heterogeneous material. Its properties depend on the properties of its

component phases and the interactions between them. If concrete is fully compacted, the

compressive strength for a given set of materials at a given age is inversely proportional

to the water/cement ratio. It has been observed, however, if the water/cementitious

material ratio and the fine aggregate/cement ratio of the concrete and mortar are constant,

the cement paste has the highest compressive strength and ductility compared to mortar

and concrete. Additionally, mortar has a somewhat higher compressive strength and a

little more ductility than concrete, but otherwise possesses a similar stress-strain curve,

figure 5.(MARTIN ET AL. 1991)

According to DARWIN (1999) the lower strength of mortar and concrete results from

stress concentrations induced in the cement paste by the aggregate particles. The stress

peaks are due to differences in the elastic properties of aggregate and paste. Failure of the

paste-aggregate interface also plays a role here, but generally to a lesser degree.

18

Figure 5. Stress-strain curves for concrete, mortar and cement paste with

a water/cementitious material ratio of 0.5 (MARTIN ET AL. 1991)

The weakness of the aggregate-matrix interface may be explained by the following

phenomena:

a) development of a higher porosity than the bulk matrix (higher w/c ratio)

b) formation of larger crystal particles of the hydration products

c) deposition of calcium hydroxide crystals with a preferential orientation on the

interface

MONTEIRO, MASO AND OLLIVIER (1985) found that the thickness of the transition phase

is determined by the intensity of the surface effects produced by the aggregate. The

thickness is larger for larger aggregates, and it is also a function of the size and shape of

the fine aggregate particles. The surface effects originated by the fine aggregate particles

interfere with those caused by the large aggregate, and the intensity of this interference

determines the final thickness of the transition zone. PING et al. (1991) discovered,

however, that for very fine limestone particles (radius ≤ 0.199 mm) the transition zone

was denser than bulk paste. They concluded this to be due to chemical reactions between

limestone particles and portland cement.

19

2.4.2 Effect of compaction degree

If the compaction of concrete is insufficient, the compressive strength is reduced.

KAPLAN (1960) observed, for example, that the compressive strength of concrete with a

voids content of 15% was reduced by approximately 72% when compared against the

strength of fully consolidated concrete This result was irrespective of the mix proportions

or the age at which the test was done. However, the reduction in strength due to a rise in

voids up to a content of 15 % was much greater than that, owing to an increase from 15%

up to 30%. The reduction percentage in concrete having a voids content of 30% was

found to be 92%. WALKER AND BLOEM (1959) concluded that, at a given water/cement

ratio the compressive strength of concrete containing up to about 10% entrained air, is

reduced by approximately 5% for every 1% of air added. Their conclusion agrees fairly

well with the results of KAPLAN (1960).

WRIGHT (1953) concluded that the effect on compressive strength is materially the same,

irrespective of whether the air is entrained intentionally in the form of numerous minute

bubbles or occurs unintentionally in the from of large irregular voids.

2.4.3 Effect of aggregate size

WALKER AND BLOEM (1960) have shown that, at a fixed water/cement ratio, strength

decreases as the maximum size of aggregate increases, particularly for sizes larger than

38 mm (1½ in.). The optimum size tends to decrease with increasing strength. This

phenomenon is caused by many parameters related to the heterogeneity of concrete, e.g.

the interface zone, lower bond stresses between the aggregate particles and the matrix,

maximum paste thickness and different dimensional changes of the paste and aggregate

at both early and later age (ALEXANDER ET AL. 1961, LALLARD AND BELLOC 1997).

However, reduction in the maximum aggregate size increases the specific surface of the

aggregates and thus the incidentally entrapped air tends to be higher or if the workability

is kept constant, the w/c ratio becomes higher. Both cases have a decreasing effect on the

compressive strength, unless the workability is controlled with superplasticizer.

20

2.4.4 Effect of aggregate strength

Most normal-weight aggregates have strengths much greater than the strength of the

cement paste. Thus, up to concrete strengths of about 35 to 40 MPa, the effects of

different good-quality aggregates are usually small. However, the aggregate strength

required is considerably higher than the normal range of concrete strengths, because the

actual stresses at the interface of individual particles within the concrete may be far in

excess of the nominal compressive stress that is applied.

In higher strength classes, the aggregate strength properties - which are also a function of

particle shape - as well as the bond between the paste and aggregate begin to play a more

important role. As the concrete is a heterogeneous material, the best compressive strength

results are achieved with aggregate, which has a high strength (e.g. a good Los Angeles

value) and low modulus of elasticity, i.e. a modulus of elasticity that is not very different

from hydrated cement paste. When the elasticity values are closer to each other, the bond

stresses are lower; thus less microcracking is induced and higher compressive strength

values can be achieved. For flexural strength, the compatibility of the modulus of

elasticity is even greater. (NEVILLE 1995)


WILLIS (1967) found that the shape of the fine aggregate had a markedly greater effect on

compressive strength than the coarse aggregate. Fine aggregate influenced the

compressive strength primarily through its effect on the need for mixing water, whereas

with coarse aggregate, other factors in addition to the water requirement affected the

compressive strength, e.g. elasticity, bond and mineralogy.

ALEXANDER (1959) concluded that if even slightly angular projections or depressions are

present on the surface of an otherwise smooth aggregate pebble, the mechanism of tensile

failure can change from a preferential rupture of the bond to a preferential rupture

through the paste in the region of the surface irregularity. (Figure 6. )

21

Figure 6. The rupture mechanism depends on the relationship between bond

and paste strengths as well as on the degree of irregularity on the surface

of the crushed aggregate particle. ALEXANDER (1959)

KAPLAN (1959) studied 13 different coarse aggregates and found, that the most important

factor in coarse aggregate affecting the compressive strength was the surface texture. A

rougher surface results in a greater adhesive force between the cement matrix and the

aggregate. In this study, the surface texture was determined by comparing the traced line

length from a magnification of 125 times against the length of an unevenness line drawn

as a series of chords.

One explanation for surface texture is the porosity of the particle surface. A porous, dry

surface absorbs water and thus positively influences the bond between the aggregate

particle and the paste. Additionally, if the aggregates are drier than SSD, the

water/cement ratio will be reduced by the absorption of the aggregates and, consequently,

the strength will increase. (STOCK ET AL. 1979, NEVILLE 1995)

When it comes to the effect of the shape, angularity and surface texture, it is somewhat

difficult to compare the results obtained by researchers, because nearly all the studies

have been conducted using different testing methods to determine the same

characteristics. Also, the terminology is overlapping to some extent, e.g. the line used to

distinguish between the surface texture and angularity is vague.

Aggregate

Paste

A C

B

B’A

B

C

22

2.4.6 Effect of aggregate surface area

STOCK ET AL. (1975) conducted tests to study the effect of the aggregate concentration

on the compressive strength of concrete. The results show that the strength of cement

paste in tension and in compression is reduced by the addition of 20% by volume of

graded aggregate, and it fell to a minimum value at a volume fraction of 30% to 35% and

then increased with a further addition of aggregate.

When the specific surface of aggregate is increased for a constant mix proportion, the

amount of cement relative to the surface of the aggregate decreases. LALLARD AND

BELLOC (1997) state that as the maximum paste thickness (MPT) between aggregate

particles decreases the compressive strength increases. In dry packing of particles, it has

been observed that the highest stresses exist at the contact points of aggregate particles.

Thus, when paste is introduced into the packing and it is placed between two close

aggregates, the paste will be highly stressed, yielding a greater matrix strength. The

results of GOBLE AND COHEN (1999) also showed that the mortar strength increased and

the strain-stress behaviour became more ductile as the quantity of the transition zone

material was increased, i.e. as the aggregate surface area was increased. They comment,

however, that increasing surface area causes stiffer mixtures, which is probably why in

the test series performed by SINGH (1958) it was noted that the increase in the aggregate

surface area caused more voids around the surface of the aggregate particles and thus a

decrease in compressive strength.


The mineral size, texture and mineralogical composition as well as the shape, angularity

and surface texture affect the strength properties of the aggregate products. Additionally,

the electrostatic conditions as well as the behaviour together with admixtures, additives

and cement depend on the mineralogy. Some chemical bond may exist between the

23

aggregate and cement paste in the case of limestone and dolomite aggregates and

possibly also siliceous aggregates. (NEVILLE 1995)

In their studies, DANIELSEN AND RUESLÅTTEN (1984) found that altered feldspars (An –

rich plagioclases) have an almost continuous transition zone from the mineral phase to

the cement paste phase. For unaltered feldspars, the contact zone was completely

discontinuous. They concluded that the altered feldspars, with their cation deficiency in

the crystal structure, make the diffusion of Ca from the cement paste into the Si-Al

framework possible. A similar phenomenon also occurs with mica minerals during

weathering. While unweathered mica (0.15/0.30 mm) caused a loss of strength in mortar,

mortar made with weathered mica didn’t deviate form the strength of the reference

mortar. Potassium leached during the weathering process helps the hydrated calcium ions

to find adsorption sites on the mica surfaces.

When mica is present in the coarse aggregate, the most important factor is not the total

amount of mica but its distribution. If the mica is in bundles, then even smaller amounts

of mica can be detrimental, though its effect can be also seen from the strength

determinations.


Superplasticizers are used to increase the workability, to reduce the w/c ratio and/or to

save cement. Changes in the w/c ratio and cement amount have clear effects on

compressive strength. By reducing the water without compromising the workability, the

24-hour early strength can be increased by 50% to 75%. Owing to the better dispersion of

the cement particles, a greater amount of reactive surface area of cement is exposed,

which can also lead to increased compressive strength. (NEVILLE 1995)

The effect of entrained air has been discussed in chapter 2.4.2.

24

2.5 Drying shrinkage

2.5.1 Mechanism of drying shrinkage

Concrete holds water in various states with different bonding energies. These are

capillary water, which is free from the influence of surface forces, adsorbed water; which

is bound to a solid surface; and interlayer water, which penetrates between a pair of solid

surfaces. Drying shrinkage is observed as a result of the forces of contraction arising as

the water is removed by drying.

There are many models for determining the drying shrinkage of concrete. There is,

however, widespread agreement that the dominant factors are the modulus of elasticity of

the aggregate and cement paste (or their ratio), the aggregate content and, aggregate and

paste shrinkage. (PICKETT, 1956, HANSEN AND NIELSEN 1965, HANSEN AND

ALMUDAIHEEM 1987)

2.5.2 Effect of water-cement ratio

Shrinkage is greater the higher the water-cement ratio is, because the w/c ratio

determines the amount of evaporable water in the cement paste and, additionally, the rate

of evaporation. BROOKS (1989) concluded that the shrinkage depends on the

water/cement ratio up to a w/c ratio of approximately 0.6, after which the additional

water in the cement paste takes the form of free water. Unlike the physically (adsorbed)

and chemically (interlayer) bound water, the free water does not contribute to shrinkage.

Hence, the change in the volume of drying concrete is not equal to the volume of water

removed.

25

2.5.3 Effect of aggregate content

The aggregate in concrete restrains the drying shrinkage; this explains the higher

aggregate content the smaller shrinkage with a constant w/c ratio. According to the

model of HANSEN AND ALMUDAIHEEM (1987), the shrinkage decreases by about 18%

when the aggregate content is changed from 65% to 70%. This change is independent of

the w/c ratio, though the restraining effect of the aggregate is more pronounced with an

increasing w/c ratio. The effect of the aggregate content on concrete shrinkage has also

been reported by, e.g. PICKETT (1956).

2.5.4 Effect of elastic modulus of aggregate

The total restraining effect of aggregate depends not only on the volume concentration of

the particles but also on the elastic properties of the particles and paste. The modulus

ratio is defined as the ratio of the elastic modulus of the dispersed particles to the

hydration products. For normal-weight concrete, the modulus ratio is typically in the

range of 4 to 7. According to the model presented by HANSEN AND ALMUDAIHEEM

(1987), the difference in dying shrinkage of concrete having a volume of aggregate in the

range of 60% to 80% is about 30% when the modulus ratio increases from 4 to 7. When

the effect of same change in the modulus ratio is predicted with the model by presented

HANSEN AND NIELSEN (1965), the decrease in drying shrinkage is, however, only 8%.

The reason for this difference between the two models lies in the calculation of Young’s

modulus of elasticity, especially, how the aggregate effect is taken into account.

2.5.5 Effect of aggregate grading, shape, size, angularity and surface texture

The effect of aggregate grading, shape and size on concrete shrinkage is indirect and

depends on how these influence the amount of water amount in the concrete. On the

other hand, aggregate properties that enhance the bond between the paste and aggregate,

26

e.g. surface texture, angularity and porosity (see 2.3.6) decrease the drying shrinkage.

(ACI COMMITTEE 221, 1997)

2.5.6 Effect of aggregate shrinkage properties

Some aggregates are known to shrink on drying. In most cases, these aggregates also

have a high water absorption. Generally, aggregates containing quartz or feldspar and

granite, limestone, dolomite as well as some basalts can be classified as low-shrinkage

producing aggregates. Aggregates containing sandstone, shale, slate, graywacke, or some

types of basalt have been associated with high-shrinkage concrete. However, the

properties of a given aggregate type, such as granite, limestone or sandstone, can vary

considerably within different sources. This can result in significant variation in the

shrinkage of concrete made with a given type of aggregate. (ACI COMMITTEE 221, 1997)

In their studies, HANSEN AND NIELSEN (1965) concluded, that if any appreciable

shrinkage occurs in the aggregate material, the restraining effect of the particles is

reduced and that it is not usually possible to bring the concrete shrinkage within

reasonable limits by adjusting the composition of the concrete mix. Similar results were

reported previously by CARLSON (1939), as can be seen from table 1.

Table 1. Drying shrinkage of concrete with different aggregates (CARLSON 1939)

Aggregate

Particle density

[ Mg/m3 ]

Absorption

[ % ]

1-year drying shrinkage,

RH 50%

[ o/oo]

Sandstone 2.47 5.0 1.16

Slate 2.75 1.2 0.68

Granite 2.67 0.5 0.47

Limestone 2.74 0.2 0.41

Quartz 2.65 0.3 0.32

27

The presence of clay on the aggregate lowers its restraining effect on shrinkage.

Moreover, because the clay itself is subject to shrinkage, clay coatings can increase the

shrinkage by up to 70%. (POWERS 1959)


If superplasticizer is used for water reduction then two opposite phenomena affect the

drying shrinkage. A lowered w/c ratio reduces the shrinkage, whereas the enhanced

dispersion of cement increases the effective surface area of the paste and thus increases

the shrinkage.

BROOKS (1989) studied five different plasticizers and superplasticizers in water reduced

and cement reduced concrete mixes and found that the admixtures increase the

deformation (shrinkage and creep) by 3% to 132% compared to plain concrete. His

suggestion was that for admixture flowing concrete (high workability), the deformation

expectation should be increased by 20%.

Entrainment of air has been found to have no effect on shrinkage. (KEENE 1960)

28

3. DATA ANALYSIS – METHODS AND EXCEL PROGRAM USED

3.1 Inputs – outputs

We studied how the fine aggregate characteristics affect the concrete properties. To be

able to relate the extent of the effect that the aggregate has on the concrete compared to

the effect of the mix design changes, the testing program was build to contain six

different mix designs in which 21 fine aggregate products were studied altogether in 215

castings. See section 4.

The fine aggregate characteristics and mix design parameters are input variables, and the

fresh and hardened concrete properties are the outputs to be modelled. (Figure 7)

Figure 7. Input – output scheme

These outputs have been modelled with the methods described in chapters 3.2 and 3.3.

The models can be used with the Excel –program described in chapter 3.4.

Additionally, concrete drying shrinkage and the air % in hardened concrete were studied,

but these were not modelled.

Mix design parameters

Fine aggregatecharacteristics

•Air %, fresh concrete•Flow value•Bleeding•Compressive strength

INPUTS OUTPUTS

29

3.2 Bayesian statistics and Gaussian processes for prediction of

the fine aggregate-concrete interaction

3.2. 1 Bayesian methods

Bayesian statistical methods use probability to quantify uncertainty in inferences. The

result of Bayesian learning is a probability distribution expressing our beliefs regarding

how likely the different predictions are. The prior information from the problem is

combined with the evidence from the data, giving the posterior probability of the

solutions. Predictions are made by integrating over this posterior distribution. The effect

of the prior information diminishes with increased evidence from the data and in the case

of insufficient data, the prior dominates in the solution. The article of GELMAN ET AL.

1995 gives a good introduction to Bayesian methods.

3.2.2 Gaussian Process

As it is not known what the parameterised form of the input-output relationship should

be, we use non-parametric non-linear Gaussian process (GP) models (RASMUSSEN 1996,

ABRAHAMSEN 1997, MACKAY 1998, NEAL 1997, NEAL 1999). In a nonparametric model,

the input-output relationship is determined from the data without reference to an

explicitly parameterised physical model. Gaussian processes are a natural way of

specifying prior distributions over possible relationships between the inputs and the

output. In material science, Gaussian Processes have been applied, e.g. to the problem of

predicting the microstructures of forged materials (BAILER-JONES ET AL. 1998) and the

austenite formation in steel (BAILER-JONES ET AL. 1999).

Based on the training data ( ) ( )( ) ( ) ( )( ){ }nn yxyxD ,,...,, 11= (having n data points), our

primary purpose is to predict the new output, ( )1+ny , for a new case where we have

30

observed only the new input vector, ( )1+nx . With Gaussian processes predictive

distribution of ( )1+ny is Gaussian, with the mean and variance given by

( )[ ] yCkDyE n 11 ´ −+ = Equation 2

( )[ ] kCkVDyVar n 11 ´ −+ −= Equation 3

where,

C is the n by n covariance matrix of the observed targets( ) ( ){ }nyyy ,...,1= is the vector of known values for these targets

k is the vector of covariances between ( )1+ny and the n known outputs

V is the prior variance of ( )1+ny (i.e. ( ) ( )[ ]11 , ++ nn yyCov ).

There are many possibilities for the covariance function, some of which are discussed in

(RASMUSSEN 1996, ABRAHAMSEN 1997, MACKAY 1998, NEAL 1999). For example, a

regression model based on a class of smooth functions can be obtained using a

covariance function of the form

( ) ( )( ) 2

1

222 exp σij

p

u

ju

iuuij dxxrsC +��

�

��

�−−= �

=Equation 4

The first term of this covariance function expresses that the cases with nearby inputs

should have highly correlated outputs. The s parameter gives the overall scale of the local

correlations. The ur parameters are multiplied by the co-ordinate wise distances in input

space and thus allow for different distance measures for each input dimension. For

irrelevant inputs, the corresponding ur should be small in order for the model to ignore

these inputs.

The second term is the noise model, where 1=ijd when i=j. For the noise model, we

tested normal and 4t distributions. The 4t distribution is Student's t distribution with 4

degrees of freedom, which is a quite safe and robust choice when the true noise

distribution is unknown.

It should be noted that this noise model is only for the outputs, and we assume here that

the inputs are noise-free. This assumption is wrong (we know there are measurement

31

errors in input variables), but we assume that this simplification still gives the model

acceptable accuracy. A noise model for the inputs would improve estimate of predictive

distribution and would allow reconstruction of the regression over the true noiseless input

– but such a noise model would be more complex to implement and to use. (CARROLL ET

AL. 1995, CORNFORD ET AL. 1998, WRIGHT 1999).

Our prior knowledge is usually insufficient to fix the appropriate values for the

hyperparameters in the covariance function (σ , s, and the ur for the model above).

Therefore the hyperparameters are given prior distributions and predictions are made by

integrating (averaging) over the posterior distribution for hyperparameters. This

integration can be done using Markov Chain Monte Carlo (MCMC) methods (GILKS ET

AL. 1996, GAMERMAN 1997, ROBERT & CASELLA 1999). In Monte Carlo methods

expectations of integrals are approximated by using a sample of values drawn from the

posterior distribution of parameters. In MCMC, samples are generated using a Markov

chain that has the desired posterior distribution as its equilibrium distribution.

We have used Flexible Bayesian Modeling (FBM) software (NEAL), which implements

the methods described in (NEAL 1996, NEAL 1997, NEAL 1999).

• The Gaussian process specification used was

gp-spec log nin 1 - - 0.01 / 0.05:0.5 0.05:0.5:1

• The noise model specification used was

model-spec log real 0.05:0.5:4

• The initial values for the model parameters were set as

gp-gen log fix 0.2 0.1

• The MCMC sampling parameters were set as

mc-spec log repeat 10 sample-variances heatbath 0.9 hybrid 10 0.15 negate

The length and the number of the chains and the burn-in length were decided using visual

inspection of trends and the potential scale reduction method (GELMAN AND RUBIN

1992A, GELMAN AND RUBIN 1992B).

32

3.2.3 Relevance values of inputs

In the GP model using a covariance function of the equation 4 the ur parameters are

sometimes called Automatic Relevance Determination (ARD) (GIBBS 1997, NEAL 1997,

NEAL 1999). The ARD parameter determines the distance to the particular direction in

the n-dimensional space (n = number of inputs) over which the data point is expected to

vary significantly, i.e. the ARD listing can be referred to as a listing of the relevance

values of the inputs.

We computed the relevance value for each input for each posterior sample of relevance

parameters ( ur ). This yields a sample from the posterior distribution of the relevance

values, which may be summarised to provide an estimate of the mean (asterisk) and

median (diamond) values for each input, plus 25%-75% (box) and 10%-90% (line)

quantiles. The quantiles describe the uncertainty of each input in relevance value. Figure

8 shows the relevance value listing of the inputs for the compressive strength 91 d model.

(See chapter 7.4.) Higher value describes a higher relevance for the specific input.

Figure 8. Example of a relevance value listing of inputs, compressive strength 91 d

Asterisk – mean value; diamond – median value;

box – 25-75 % quantiles; line - 10-90 % quantiles

WR

Flkn 3.15/4.0 mm

AE

Los Angeles

QNTY 1.6/2.0 mm

Pore area 10-300 Å

SEM

-4 -3 -2 -1 0 1

33

3.2.4 Deviance Information Criterion (DIC) for model evaluation

The purpose of interpolation problems is not usually to obtain the closest fit to the data

but to find a balance between fitting the data and making sensible predictions about new

events. Hugely complex models are often over-parameterised and, while fitting the data

precisely, they interpolate and extrapolate poorly. Within the classical modelling

framework, model comparison takes place by defining a measure of fit, typically the

deviance statistic, and complexity, the number of free parameters in the model. (GIBBS

1997, SPIEGELHALTER ET AL. 1998)

Deviance Information Criterion (DIC) was recently proposed by SPIEGELHALTER ET AL.

(1998) for comparison of arbitrarily complex Bayesian models.

DIC is based on comparison of the posterior distribution of the deviance

( ) ( ) ( )yfypD log2log2 +−= θθ ,

where y is the observed data and θ are the lowest-level parameters directly influencing

the fit. The standardising term ( )yf is a function of the data alone and hence does not

affect model comparison.

The fit of a model is summarised by the posterior expectation of the deviance

[ ]DED yθ= .

The model complexity is measured by the effective number of parameters Dp , defined as

[ ] [ ]( )θθθ yyD EDDEp −=

( )θDD −=

34

The fit and the complexity are then added to form a Deviance Information Criterion

DpDDIC +=

( ) DpD 2+= θ

The DIC and quantiles for it can be easily obtained from the MCMC analysis.

3.2.5 Data pre-processing

For computational reasons, input and output variables were normalised to have zero

mean and unit variance (BISHOP 1995 P.298, NEAL 1999). Some of the outputs (air %,

bleeding 60min) had values close to zero, but it is known that the values for these outputs

are always greater than zero. In order to assure that the predictions and predictive

quantiles for these outputs would always be greater than zero, log transformation was

used.

3.2.6 Model selection

First we made models with different noise models for each output with full set of

potential inputs (see chapter 4.4.). To compare different noise models, we calculated the

mean square error (MSE) and 90% quantiles of absolute error of the test data and

Bayesian Deviance Information Criterion (DIC). The 4t noise model was clearly better

than the Normal noise model for all outputs. Then, using relevance values of the inputs,

smaller sets of inputs were selected and new models were made (some inputs were

favoured over others, based on expert knowledge e.g. BET vs. pore area (fines); see

chapter 4.4.

We continued this approach for each output until the DIC increased. The best model

according to the DIC was selected and then, using backward selection, the input set was

still reduced. The model with the lowest DIC was selected. If several models had

statistically similar DIC values, the model with the least inputs was selected. Models

35

having the least inputs had similar errors compared to the errors of the model having all

inputs. Depending on which output was modelled, seven to twelve input variables were

needed. (see section 7.)

3.2.7 Model errors (prediction errors)

To estimate prediction errors we used a ten-fold cross-validation (10-CV) error estimate,

i.e. nine tenths of the data was used for training and the one tenth was left out for error

evaluation, and this scheme was repeated ten times (STONE 1974, GEISSER 1975,

GELFAND AND DEY 1994). All the castings were used for inferences, but error estimates

were computed only for castings with A and B aggregate products (no REF was used).

Quantiles of estimated prediction errors were obtained by re-sampling. Cross-validation

was used to produce cross-validation predictive densities (GELFAND 1996). Expectations

and quantiles were then easy to estimate by re-sampling MCMC samples and data points.

3.3 Excel program for prediction of fine aggregate – concrete interaction

3.3.1 General principles of the Excel program

When the desired input combination (fine aggregate characteristics and mix design

parameters) are entered into the Excel program, it will

• calculate the expectation value for the output and 10% and 90% quantiles for the

prediction (→ 3.3.2)

• suggest adjustments to other input variables when one variable is changed (→ 3.3.2)

• show how marginal changes of one input affect the specific output, i.e. it

demonstrates, the output sensitivity to an input variation (→ 3.3.3)

• show the reliability of the sensitivity analysis (→ 3.3.3).

36

3.3.2 Predicting the correlation in input variables

When a single input variable is changed by a large amount, we would like to take into

account the correlation between the inputs. All other inputs should be adjusted in such a

way that the new input vector is similar to those found in the training data set. This can

be done by calculating the covariance matrix of the data and adjusting the other inputs

according to the relative magnitude of the elements in the covariance matrix,

2

2

,,ii

ijiunchangedjchangedj dxxx

σσ

⋅+= . Equation 5

where

i is the index of the manually changed input

idx is the change made to that input

j is the index of the input to be adjusted

2ijσ is one element in the covariance matrix Σ

For practical purposes, it is better to use a regularised estimate for the covariance matrix.

This is done by Principal Component Analysis (PCA) (BISHOP 1995). First, the

maximum likelihood estimate for the covariance matrix is computed with

�=

−−=ΣN

i

Tii xxN 1

)()( ))((1 µµ ��

�

. Then, the eigenvectors iv and eigenvalues iλ of the

matrix Σ�

are computed, choosing M largest ones. The regularised estimate for the

covariance matrix is then TVVΛ=Σ~ , where the matrix Λ has M largest eigenvalues iλ

on the main diagonal and the matrix V contains corresponding eigenvectors iv as

columns. Using a regularised estimate has the advantage of making more conservative

adjustments to the inputs because less significant and noisy correlation effects are

ignored.

37

3.3.3 Sensitivity analysis and its reliability

Sensitivity analysis answers the question: “How does the output change when an input

changes?” At each data point, a single input variable is changed by a small amount (for

example, ±2%) and the predicted output is calculated. By comparing the change made in

the input variable to the change perceived in the output, we see how the model reacts to

changes in that particular input variable. A useful graph can be made by plotting the

input variable on the horizontal axis and connecting the predicted outputs of the original

and changed inputs with a line (Figure 9).

Figure 9. Example of an input-output sensitivity analysis

The slope of this line then represents the sensitivity of the model in one data point. If the

lines are horizontal, the change in the input variable has no effect on the output. Upward

and downward slopes suggest positive and negative effects in the output, respectively.

Having two data points with different slopes close to one another does not necessarily

mean that the model is incorrect; the change in the slope could be due to a large change

Sensitivity analysis

35

40

45

50

55

60

65

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Co

mp

. Ste

ng

th [

MP

a]

N30

WR30AE30N35WR35AE35

38

in other input variables, not illustrated in any way in the graph. The mutual correlation of

the input variables is ignored in this analysis, as the changes made in the inputs are small.

We would also like to estimate the reliability of our sensitivity analysis. By plotting the

input variable of interest on the horizontal axis and connecting the predicted and

measured output variables on the vertical axis with a line, we can identify ranges in the

input variable where prediction errors are large, and thus where the model is not to be

trusted (Figure 10).

Figure 10. Example of the reliability of a sensitivity analysis

In these areas, the results of the sensitivity analysis are likely to be incorrect as well. In

contrast, where the prediction errors are small, the results of the sensitivity analysis are

deemed to be more plausible.

Sensitivity analysis - difference between modelled and measured values

35

40

45

50

55

60

65

70

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Co

mp

. Ste

ng

th [

MP

a]

N30WR30AE30N35WR35AE35

39

3.4 Error estimation

For determining the error in output measurements, we repeated 15% of the castings.

From these repetitions we determined the repeatability error of measurement; it is the

median of the 90% quantile of the absolute error. It includes the error made by the

laboratory personnel as well as errors arising from differences in the conditions during

the repetitions. In addition, it includes the deviations within the raw materials between

the repetition castings. The skewness of the error prediction distribution can be observed

from the 10/50/90% quantiles of the 90% quantile of the absolute error. In the text is

used notation, e.g. “40 mm (30 – 60 mm)”, in which the 40 mm is the median (50%) of

the 90% quantile of the absolute error, and the values in parentheses give the 10% and

90% quantiles for the 90% quantile of the absolute error.

The model error includes the repeatability error of the measurement and the errors due to

the selected inputs and model. As discussed earlier, the model selection is normally done

by choosing the model where the fit and complexity are optimised. The input selection is,

of course, crucially important to the model performance. If the inputs aren’t capable of

describing the output phenomenon, then the lack of the fit of the model will be obvious

and hence the model error will be large. Also, the model error describes the median of

the 90% quantile of the absolute error and the notation in the text is similar to the

repeatability error notation.

The group average is the median of the 90% quantile for the mix design group results,

i.e. castings without admixture (N), with superplasticizer (WR) and with air-entraining

agent (AE). The largest group is that, where all the castings are included (all). When we

compare the group average to the repeatability error, we can evaluate the variation

caused by other factors, presumably that caused by the fine aggregate characteristics.

40

4 EXPERIMENTAL PROGRAMME

4.1 Materials

4.1.1 Aggregate products

Fine Aggregate products

A total twenty-one (21) different aggregate products were used in this study. Thirteen

(13) of these were gravel materials and eight (8) crushed rocks. Table 2 is presents the

aggregate products, their material source, rock type, used size fractions and geographical

origin.

Table 2. List of the aggregate products

Aggregate

Material

source Rock type

Used size fractions

fines / semi-coarse

Geographical

origin

A2 Rock Granite F & SC Uusimaa

A3 Rock Granite F & SC Uusimaa

A6 Rock Mica gneiss F Pohjois-Savo

A7 Rock Tonalite F & SC Uusimaa

A8 Rock Garnet bearing granite F & SC Pohjanmaa

A10 Rock Tonalite SC Pohjanmaa

A15 Rock Mafic metavolcanite F Pohjois-Savo

A16 Rock Gabbro F & SC Kanta-Häme

B1 Gravel Granitic gravel F & SC Uusimaa


B3 Gravel Granitic gravel F Kymenlaakso

B6 Gravel Granitic gravel F Varsinais-Suomi

B7 Gravel Granitic gravel F & SC Pohjois-Savo

B8 Gravel Sandstone, granitic gravel F & SC Satakunta

B9 Gravel Sandstone F Satakunta

B10 Gravel Granitic gravel F Satakunta


B12 Gravel Granitic gravel F & SC Päijät-Häme

B13 Gravel Granitic gravel F & SC Päijät-Häme


REF Gravel Granitic gravel SC Uusimaa

41

Aggregate REF is the normal concrete laboratory aggregate used in Finland and was

chosen as a reference aggregate for this study.

More accurate mineralogical compositions of the aggregate products are presented in

tables 12 - 14 in chapter 5.

In order to control the mix design grading curve, the aggregate products were sieved to

six (6) nominal size fractions; 0/0.125, 0.125/0.25, 0.25/0.5, 0.5/1.0, 1.0/2.0 and 2.0/4.0

mm. Down to 0.25 mm the sieving was performed with a Mogen sieving apparatus, and

the two smallest size fractions were separated using a Sweco sieving machine. To ensure

accurate sieving results the size fractions 1.0/2.0 and 2.0/4.0 mm were sieved twice and

the size fractions 0.25/0.5 and 0.5/1.0 mm and 0/0.125 and 0.125/0.25 mm three times.

The REF aggregate was already sieved to narrow nominal size fractions; 0.1/0.6, 0.5/1.2,

1/2, 2/3 and 2/5 mm. Only the finest size fraction, 0.1/0.6 mm, was sieved using the

Sweco to obtain the fraction 0.125/0.6 mm. Appendix 1 presents the original gradings of

the aggregate products.

Coarse aggregate

The coarse aggregate was a combination of two coarse aggregate products. The nominal

sizes of the aggregate products were 5/10 and 8/14 mm. Both aggregate products were

uncrushed granitic gravels from Uusimaa.

4.1.2 Cement

The cement was Finnish rapid hardening cement, CEM IIA 42.5R. Table 3 presents its

chemical composition and physical characteristics. As it was already known from the

beginning that the total time for the castings would be long, extra care was taken to

prevent the cement from ageing. To verify the quality of the cement along the castings,

four (4) strength determinations were made. Table 4 shows the initial compressive

strength results and the three determinations done during the castings.

42

Table 3. Chemical composition and physical properties of cement

Chemical composition [ % ] Physical properties

CaO 61.3 Property Value

SiO2 19.6 Particle density 3.14 Mg/m3

Al2O3 4.8 Specific surface (Blaine) 473 m2/kg

MgO 3.0 Standard consistency 31.0 %

Fe2O3 2.4 Setting time, beginning 90 min

SO3 3.6 Setting time, end 150 min

Na2O eq 1.3

TiO2 0.3

Table 4. Compressive strength determinations

Age [ d ] Compressive strength [ MPa]

25.02.1998 25.08.1998 30.11.1998 08.02.1999

1 24.8 25.4 29.1 28.9

7 49.6 49.9 51.3 49.7

28 57.1 58.1 57.9 57.0

4.1.3 Admixtures

Two admixtures were used: a superplasticizer and an air-entrainment agent. The

superplasticizer was a sulphonated naphthalene formaldehyde condensate, Mighty 150. A

fatty acid soap-based product, Ilma-Parmix, was used as an air entrainment admixture.

The air entrainment agent was diluted to solution of 1:19. Both substances are

commercially available and commonly used admixtures in the concrete industry.

The dry material content for the water reducer is 42% and for the diluted air-entraining

agent 5%.

43

4.2 Test programme

4.2.1 Aggregate (inputs)

The test programme was divided into tests for the fines (F) and semi-coarse (SC) size

fractions separately partly because this is natural due to the magnitude difference

between them, but also in order to ascertain the effects of the characteristics in isolation

from each other. This is especially important with the processed fine aggregate products,

e.g. mixture of uncrushed gravel and rock products. For the purpose of better revealing

the variations in the characteristics, the fines were tested as the size fraction 0/0.063 mm.

Table 5 presents a list of the basic characteristics of the fine aggregate studied and the

test methods used. It also shows to which size fractions the tests have been applied.

Table 5. Basic characteristics of the fine aggregate studied and the test methods

used

Basic characteristic of the

fine aggregate Test method Tested size fraction(s)

Mineralogical composition Röntgen diffraction

(X –ray)

0/0.063 mm

2.0/4.0 mm

Specific surface area 1. BET-analysis

2. Laser diffraction 0/0.063 mm

Grading Laser diffraction 0/0.063 mm

Particle density Helium pycnometer 0/0.063 mm

2.0/4.0 mm

Particle porosity Mercury intrusion porosimetry 0/0.063 mm

0.5/1.0 mm

Zeta potential Zeta potential 0/0.063 mm

Resistance to fragmentation Los Angeles value, modified 4.0/5.6 mm

44

Elongation Image analysis 0.8/1.0 mm

1.6/2.0 mm

3.15/4.0 mm

Flakiness Image analysis together with

average particle volume

measurement

0.8/1.0 mm

1.6/2.0 mm

3.15/4.0 mm

Angularity/roundness Image analysis 0.8/1.0 mm

1.6/2.0 mm

3.15/4.0 mm

Surface texture Image analysis 0.8/1.0 mm

1.6/2.0 mm

3.15/4.0 mm

4.2.2 Concrete (outputs)

The total quantity of castings was 215, which were divided into six different mix designs.

Two different cement amounts, 300 kg/m3 and 350 kg/m3, were studied without

admixtures and with two admixtures, superplasticizer and air-entraining agent. The

quantity of the castings using different mix designs are presented in table 6.

Table 6. The quantity of castings using different mix designs

Mix

design

Cement amount

[ kg/m3 ] Admixture Number of castings

N30 300 no admixture 34

N35 350 no admixture 41

WR30 300 superplasticizer 38

WR35 350 superplasticizer 30

AE30 300 air-entraining agent 29

AE35 350 air-entraining agent 43

45

The drying shrinkage and weight loss measurements were mainly performed on mix

designs N35, WR30 and AE35 and there are therefore a greater number of castings with

these mix designs.

In order to determine deviations between castings, 15 % of them were repeated (see

appendix 2). In addition, two mixes were repeated several times (5 and 6 times) along the

castings to ascertain that no major changes had occurred during the one year that it took

to conclude all the castings.

As far as deviations within castings were concerned, the focus was on compressive

strength results. Each compressive strength result is an average of three (3) cubes; this

also applies to the results for density, because the same cubes were used for the density

measurements.

The concrete test programme consists of tests for both fresh and hardened concrete. The

tests were selected so as to cover the majority of the building code requirements and the

practical concrete tests performed during concrete production. They were also designed

to reveal potential difficulties arising from variations in the aggregate inputs. The lists of

tests are presented in tables 7 and 8 for fresh and hardened concrete respectively.

Table 7. Measured characteristics of fresh concrete and test methods

Concrete

characteristics Test method

Testing age

(after mixing) Remarks

Workability Slump

Flow value

5 min

7 min

All castings

All castings

Density of fresh concrete

Unit mass of the

concrete in 8 l container 10 min All castings

Air %, fresh concrete Pressure method 12 min All castings

Bleeding Bleeding test 10, 30, 60 min All castings

46

Table 8. Measured characteristics of hardened concrete and test methods

Concrete

characteristics Test method Testing age Remarks

Compressive

strength

Compressive strength

measurement

24 h

28, 91 d All castings

Density of hardened

concrete

Particle density 24 h

28, 91 d All castings

Drying shrinkage Measurement of

the length changes Up to 231 d

64 of 215

castings

Weight loss Measurement of

the weight changes Up to 231 d

64 of 215

castings

Air %,

hardened concrete

Determination from

the thin section 56 ± 2 d

(AE mix designs)

72 of 215 castings

Specific surface area

of air void

Determination from


(AE mix designs)

72 of 215 castings

Spacing factor

of air void

Determination from


(AE mix designs)

72 of 215 castings

4.3 Mix designs and concrete mixes, mixing procedure, test specimens

4.3.1 Mix designs and concrete mixes

Mix designs

Six (6) different mix designs were applied to all 215 castings, as shown in table 9. The

designs consisted of two different cement amounts, corresponding to low and high paste

volumes, together with three admixture classes; no admixture, superplasticizer or air-

entraining agent.

47

The mix designs were based on the following rules:

1. Two cement amounts, 300 and 350 kg/m3

2. Same water-cement ratio for both cement amounts

3. Both cement amounts without admixture (N), with superplasticizer (WR) and

with air-entraining (AE) agent

4. Same starting workability with one fines/semi-coarse aggregate combination in

low paste mix designs (B3/B14, slump 110±10 mm)

5. Same superplasticizer dosage % from the cement amount in the WR mix designs

6. Same air-entraining agent dosage % from the cement amount in the AE mix

designs

7. The water amount includes the water in the admixtures

8. Same fines % for the cement amount in all mix designs

9. Same combined grading curve for all mix designs, though fines amount (passing-

% 0.125 mm) varies according to rule # 8

Table 9. Mix designs

Mix

design

Cement

amount

[ kg ]

Water

[ l ]

Aggre-

gates

[ l ]

Fines

[ kg ]

20%C

Super-

plasticizer

[ kg ]

1.2%C

Air-entr.

1:19

[ kg ]

0.425%C

Air

[ % ]

W/C

ratio

Paste

amount

[ % ]

Paste

amount

[ % ]

w/ air

N30 300 195 700 60 - - 1 0.65 31.2 32.2

N35 350 228 651 70 - - 1 0.65 36.5 37.5

WR30 300 171 724 60 3.6 - 1 0.58 28.8 29.8

WR35 350 200 679 70 4.2 - 1 0.58 33.7 34.7

AE30 300 174 679 60 - 1.3 5 0.58 29.1 34.1

AE35 350 203 634 70 - 1.5 5 0.58 34.0 39.0

The mix designs were made with the assumption that the air % for the N and WR mix

designs would be 1% and for the AE mix designs 5%. However, it was known that there

would be great deviations between the actual measured values and the theoretical values.

Actually, this is one of the points of interest in this study, and it has to be taken into

account in the interpretation of the other results, e.g. compressive strength and

slump/spread values.

48

Combined grading curve

In all mix designs, the ratio of the fine and coarse aggregates was kept constant. The

percentage of the fine aggregate in the total aggregate was always 42.8 % and thus that of

the coarse aggregate was 57.2 % (5/10 mm 20.2 %-unit and 8/14 mm 37.0 %-unit). The

target combined grading curve for all the mix designs is presented in table 10. The

passing-% of the 0.125 mm sieve is dependent on the cement content and is further

slightly affected by the total aggregate amount of the mix designs. The target value varies

from 3.1% to 4.1%.

Table 10. Target combined grading curves for all mix designs

Mix

design

0.125

mm

0.25

mm

0.5

mm

1

mm

2

mm

4

mm

8

mm

14

mm

16

mm

N30 3.2

N35 4.0

WR30 3.1

WR35 3.8

AE30 3.3

AE35 4.1

8.9 15.0 23.0 32.5 46.5 60.0 95.0 100

Fine aggregate combinations

The fine aggregate products had been sieved into narrow size fractions: i.e. fines, 0/0.125

mm, and five semi-coarse fractions, 0.125/0.25, 0.25/0.5, 0.5/1.0, 1.0/2.0 and 2.0/4.0

mm.. Figure 11 presents the ways in which the fine aggregate fractions were combined.

The combination of the fines and semi-coarse fractions could range from:

• the same aggregate or

• two different aggregate products or

• a maximum of 4 different aggregate products both for the fines

and semi-coarse fractions

49

Figure 11. Combinations of the fine aggregate combining;fines and semi-coarse size fractions

Concrete castings

The list of all concrete mixes for the 215 castings with mix design type and fine

aggregate combinations is contained in appendix 2. The list also indicates repetitions of

castings.

4.3.2 Mixing procedure

The castings were mixed using a 50 litre Zyklos pan mixer, and the size of the batches

varied between 27 and 32 litres. A larger batch size was needed when shrinkage and

weight loss prisms were also cast.

The filling order of the mixer was: coarse aggregate products, cement and fine aggregate

products. The following figure 12, illustrates the mixing cycle. The total mixing time was

five minutes.

<0.125 mm

0.125 mm - 4 mm

A 100 %

A 100 %

A 100 %

B 100 %

C-D-E-Fmax 4 pc.

G-H-I-Jmax 4 pc.

50

Figure 12. Mixing cycle, total mixing time five minutes

4.3.3 Test specimens

For the purpose of compressive strength determinations, nine 100 mm cubes were cast

from each batch. The strength determinations were conducted on three ages: 24 h, 28 d

and 91 d, each representing an average of three cubes. The same cubes were used for

density measurements.

One additional cube was cast for the preparation of thin-section samples. An impregnated

pre-sample was made from each cube at the age of 56 ± 2 days, and only from the AE

mix designs were made final tin-section samples. Other pre-samples were stored as a

reserve information source in case some phenomenon might need extra clarification.

For the drying shrinkage and weight loss determinations, two 100 x 100 x 500 mm

prisms were needed. The determinations were made for 64 out of 215 castings. The

measurements consisted of drying shrinkage and weight loss determinations at ages of up

to 231 days.

Coarse aggregate, cement,fine aggregate

Dry mixing time,total 1 min

1 min

2/3 of the water

2 min

1/3 of the water +possible admixture

5 min

Wet mixing time,total 4 min

TOTAL MIXING TIME FIVE MINUTES

51

4.4 Testing methods and potential input values, aggregates

4.4.1 Mineralogical composition, fines and semi-coarse fractions

The mineralogical composition of the fine aggregate products was determined, using the

X-ray diffraction method, at the Geological Survey of Finland. For all the aggregates the

determination was made for two size fractions, <0.063 mm and 2.0/4.0 mm. Both size

fractions were ground to fine powder before sample preparation. The <0.063 mm size

fraction was additionally tested using two oriented mounts for enhanced determination of

clay minerals. From these concentrated and oriented samples the X�ray diffraction

spectrum was determined with the 2θ-angle region 2°-20°. After analysis, one sample

was treated with ethylene glycol for 24 hours and the other was heated for 1 hour at a

temperature of 550 °C. Following this, the X�ray diffraction spectrum was recorded for

the treated sample. By comparing the prior and post-treatment X�ray spectra with the

information obtained from infrared spectrometric analysis of untreated material, it was

possible to recognise and identify even minor quantities of clay minerals. A semi-

quantitative estimation of individual minerals was done by using experimentally obtained

absorption coefficients.

The potential input values gained from the mineralogical composition:

1. F and SC – clay, (clay amount, fines and semi-coarse), [ % ]

2. F and SC - mica, (mica amount, fines and semi-coarse), [ % ]

3. F and SC – amphibole, (amphibole amount, fines and semi-coarse), [ % ]

4. F and SC – quartz, (quartz amount, fines and semi-coarse) [ % ]

4.4.2 Specific surface area, fines

The specific surface area of the fines, <0.063 mm, was determined using a NOVA � 1000

Gas Sorption Analyser by the Quantachrome Corporation (BET method) and Coulter�s

LS Particle Size Analyser, which is based on the laser diffraction (LD) principle.

52

BET method

The basis of adsorption method is the fact that the amount of gas adsorbed on a gas-solid

interface under specific conditions is proportional to the interfacial area presented.

The most commonly used adsorption method is the adsorption isotherm derived by

Brunauer, Emmet and Teller (BET) using simplifying asusmptions; the isotherm gives

the amount n of gas adsorbed relative to the monolayer amount nm as a function of (1) the

gas pressure p divided by the saturation vapour pressure ps and (2) a constant C that

depends on the adsorption energy.

( ) ( )( )[ ] 1//1/1// −+−−= ssssm pCppppppCpnn Equation 6

The BET equation has generally been found to be very useful for physical adsorption for

non-porous solids, e.g. aggregates, in the pressure range 0.05 < p/ps < 0.35, and it is

usually satisfactory to determine just a single point near the upper limit of validity of the

BET isotherm. Since the constant C for nitrogen gas is generally much greater than unity

(generally 50-250), 1-p/ps, can be neglected compared with C p/ps, and the BET

adsorption isotherm is reduced to the relation:

( )sm ppnn /1−= Equation 7

The monolayer capacity nm can be calculated using equation Q* and the ideal gas

equation and thus, the equation becomes

( )sm ppRT

pVMn /1−= Equation 8

where,

p = ambient pressure (atm)

T = ambient temperature (°K)

R = gas constant (82.1 cc atm/°K mole)

V and p/ps are measured values

53

The total surface area St of the sample can be expressed as

MNAnS camt /= Equation 9

where,

N = Avogadro�s number (6.023 x 1023 molecules/mol)

Aca = 16.2 Å2, cross-sectional area for the

hexagonal close-packed nitrogen at 77 °K (temperature of liquid nitrogen)

M = molecular weight of the adsorbate (nitrogen (N2) 28 g/mol)

By combining the equations Q* and Q* the total surface area equation becomes a form:

( )RT

pppVNAS scat

/1−= Equation 10

The degree of physical adsorption increases when temperature decreases. Thus, the

nitrogen gas is allowed to adsorb on the sample surface at the temperature of liquid

nitrogen (77 °K). When the sample is transferred to ambient temperature, the adsorbed

nitrogen gas starts to evaporate from the surface of the sample and this volume of

desorbed gas is measured (RUMPF1990). The specific surface area is obtained when the

total surface area is divided by the mass of the sample. Prior testing the samples were

dried at 80 º C.

LD method

Laser diffraction (LD) is a method where the particle size distribution is determined from

the light scattering information of different size particles.

A sample, which has been dispersed either in gas or liquid, is lead through a coherent

light (laser beam). When the coherent light meets the particle surfaces, the light scatters

and thus, a diffraction pattern is formed. Both the scattered and unscattered light is then

focused to a detector plane through a transform lens, figure 13.

54

Figure 13. Optical arrangement of laser diffraction method to obtain size distribution

of particles (FELTON 1990)

The unscattered light is focused to a point on the optical axis and the scattered light

forms a pattern of rings around the central spot. The diffraction pattern is the net

diffraction of all the separate particles. Movement of the particles does not cause

movement of the diffraction pattern, because light scattered at an angle θ, will always

give the same radial displacement in the detector, irrespective of the particles position in

the illuminating beam. This diffraction pattern produced is known as the Fraunhofer

diffraction pattern. From the diffraction pattern can thus, be determined the particle size

distribution. (FELTON 1990, STANLEY-WOODS AND LINES 1992)

In the Fraunhofer diffraction calculations, it is assumed that all the particles are spheres.

As the spheres are the only particles having an equal diameter when measured either

from projected area or volume, it is obvious that the shape of the particles affect the

achieved result. For irregular particles the grading result depends on the orientation of the

particles. The particles are detected as a set of spheres having the average diameter

between the smallest and largest projected area diameter thus, generally causing wider

span in the grading curve and possibly transfer of the average particle diameter.

(WEICHTER 1986)

The samples were tested in water dispersion. To ensure proper dispersion the samples

were first wetted in a beaker by a small amount of water after which the beaker was

placed in an ultrasonic bath for one minute.

55

The main difference between the two methods is that the BET method determines the

actual surface area, including the surface area of accessible pores, by measuring the

amount of adsorbed gas on the gas-solid interface, while in the LD method the area is

calculated from the grading curve, with the assumption that all the particles are spheres

and non-porous.

The potential input values are (used denotation in bold):

1. F - BET value (specific surface area by BET, fines), [m2/g]

2. F - LD value (specific surface area by LD, fines), [m2/g]

4.4.3 Grading, fines

The grading of the fines, <0.063mm, was determined by means of laser diffraction

analysis. The equipment used was a Coulter LS Particle Size Analyser.

The principle of the method is described in chapter 4.4.2.


1. F - Hf, [%], is the sum of the passing-% for sieve sizes of

2, 4, 8, 11.2, 16, 22.4, 31.5, 45, 63, 80, 125 µm, fines

2. F - 0.008 mm, [% ], is the passing-% for the sieve size of 8 µm, fines

3. F - Cu, [ - ], is the ratio of the sieve sizes for which the passing-% is 60% and

10%, fines

4.4.4 Particle density, fines and semi-coarse fractions

The particle density measurements were made for size fractions <0.063 mm and 2.0/4.0

mm using a Quantachrome helium pycnometer, AccuPyc 1330.

The principle of the Helium pycnometer method is that the volume of a sample is

determined by measuring the pressure change of helium in a calibrated volume. The

56

density can be calculated when the mass of the sample is given. Prior testing the samples

were dried at temperature of 80 º C.


1. F – density (particle density, fines), [ Mg/m3 ]

2. SC – density (particle density, semi-coarse fractions), [ Mg/m3 ]

4.4.5 Particle porosity, fines and semi-coarse fractions

The particle porosity values are based on mercury intrusion porosimetry measurements

and were conducted using a Micrometrics Poresize 9320. The determinations were made

for the size fractions <0.063 mm and 0.5/1.0 mm.

The principle of the method is based on the fact that mercury behaves as a non-wetting

substance towards most materials. Consequently, it does not penetrate into the pores of

the material and one must apply pressure to make it do so. The most commonly applied

intrusion equation is the Washburn equation, which states that the pore size is inversely

proportional to the applied pressure:

( ) Pr LV /cos2 θσ= Equation 11

where,

P = pressure applied to the mercury

LVσ = surface tension of the mercury surface

θ = contact angle of the mercury

r = radius of the capillary

If the mercury intrusion method is applied to loose powder material, the result obtained

includes both the accessible pores in the particles (intra-particle voids) as well as the

voids between the particles (inter-particle voids). However, if the loose material is of

single size, granular material the mercury fills the inter-particle voids space

predominantly without pressure (VAN BRAKEL ET AL. 1981, KLOUBEK 1994) and thus, the

result obtained represents intra-particle voids, i.e. particle surface porosity. The former

57

case applies to the tested aggregate fines (<0.063 mm) and the latter case to the tested

aggregate semi-coarse fraction 0.5/1.0 mm.

A contact angle of 130.0° and a surface tension of 485 mN/m were used in the

calculations. Prior testing the samples were dried at 105 °C and then evacuated in a

penetrometer before mercury filling to minimum 3 Pa. The measured range of pores was

from 300 µm (3 Pa) down to 6 nm (200 MPa).

The potential input values are (used denotation in bold) :

1. F – avg. pore size (average pore size, fines), [µm]

2. F – tot. pore area (total pore area, fines), [ m2/g ]

3. SC – avg. pore size (average pore size, semi-coarse), [µm]

4. SC – tot. pore area (total pore area, semi-coarse), [ m2/g ]

5.-7. SC – pore area 60-300Å/300-900Å/>900Å

(incremental pore area 60-300Å, 300-900Å and >900Å, semi-coarse), [ m2/g ]

4.4.6 Zeta potential, fines

The Zeta potential measurements were performed for the fines, <0.063 mm, using a

Coluter Delsa 440.

Because the zeta potential measures the average electric charge of the aggregate particle

surfaces, the tests were carried out for samples without admixtures (N), with

superplasticizer (WR) and with air entrainment agent (AE). The dosages of the

admixtures were according to the mix designs, i.e. 0.2% and 0.07% from the amount of

aggregate for the WR and AE measurements respectively.

In order to obtain results that can be related to the environment of the concrete, the

electrolyte was made of 4 litres of ion-exchanged water and 1.00 kg of cement. After

mixing, the electrolyte was filtered twice, first with a coarse filter and then with a 0.22

µm membrane filter. The pH value of the electrolyte was 12.5. The zeta potential

58

measurements were conducted by means of the following routine: 100 mg of fines was

added to a beaker containing 100 ml of electrolyte. The beaker was placed in an

ultrasonic bath for one minute to ensure proper dispersion, after which the beaker was

placed on a magnetic stirrer. The sample was allowed to balance itself for 10 minutes

before measurements were made. Before any admixture addition, the N (no-admixture)

value was measured to ensure that the sample was clean of any impurities. After the

admixture had been added, the sample was again allowed to balance itself for 10 minutes

before measurements were conducted.

The potential input value is (used denotation in bold):

1. F - Zeta pot. (Zeta potential value, fines), [ mV ]

4.4.7 Resistance to fragmentation, semi-coarse fractions

The European standard EN 1097-2: Methods for the determination of resistance to

fragmentation was applied. A slight modification was made to the tested size fraction, i.e.

the size fraction was 4.0/5.6 though the smallest size fraction given in the EN standard is

4.0/8.0 mm. The shape properties of the aggregate products were not altered, e.g. by bar

sieving, and thus the results resemble the product characteristics and not only the raw

material characteristics.

The potential input value is (used denotation in bold):

1. SC - LA value (mod.) (Los Angeles value (mod.), semi-coarse), [ % ]

4.4.8 Elongation, flakiness, particle volume and quantity, semi-coarse fractions

The determination of elongation, flakiness, particle quantity, surface area, angularity and

surface texture area were all based on scanned images. The scanner was a normal office

scanner (AGFA SnapScan 600). For image processing, an image analysis tool was

developed.

59

The pre-processing of the scanned images

The images were scanned in colour, and thus they are first converted to greyscale. The

aggregate particles are recognised from the background using simple thresholding. The

grey level distribution of the image has two peaks, one corresponding to the background

and one to the particles. The minimum between the two peaks in the histogram marks the

decision boundary: pixels which are darker than this boundary belong to the background

and pixels which are lighter belong to particles. In order to reduce noise, the binary

image produced by means of thresholding is then filtered using a 3-by-3 median filter.

This operation marks each pixel black or white, depending on which was more common

in the pixel�s 3-by-3 neighbourhood. Next, a morphological opening operation is

performed. This tends to even out the particle boundaries and removes small holes,

caused by speckles in the image, from the particles. Finally, connected areas in the image

are sought and holes possibly still left inside these areas are filled.

The determinations of elongation and flakiness were conducted for three narrow size

fractions: 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm. The same procedure was applied in the case

of each size fraction:

a. The theoretical geometric mean for the sphere diameter of the narrow size

fraction was calculated (e.g. 3.15/4.0 mm → 3.55 mm)

b. Particles of the narrow size fraction were spread on a scanner screen

c. The image of the particles was scanned with a resolution of 1000 pixels per inch

d. The quantity and the areas of the particles were determined by an image analysis

program. (The quantity was cross-checked by manual counting)

e. The average area of one particle from the narrow aggregate size fraction was

calculated and the pixels were transformed to mm value

f. From the average area was calculated

the average equivalent 2D diameter (=circle)

h.

g.
ELONGATION =
[ avg. equivalent 2D diameter / theoretical sphere diameter ]

As the quantity of the scanned particles and the particle density are known → the

average particle volume → the average equivalent 3D diameter (=sphere)

60

The part

means o

( Total a

The perc

was the

8.0% fo

The elo

aggrega

The pote

1. S

(

2. S

(

3. S

(

4. S

(

5. S

4.4.9 A

Angular

approxim

shaped

pixels fr

i.
FLAKINESS =
[ avg. equivalent 2D diameter / avg. equivalent 3D diameter ]

icle quantity per each size fraction was calculated from the mix information by

f the equation:

mount of aggregates [ l ] * Percentage of size fraction [ % ] )

Average particle volume of size fraction [ dm3 ]

entage of the size fraction was calculated from the combined grading curve and

same for all mix designs i.e. 14.0% for 3.15/4.0 mm, 9.5% for 1.6/2.0 mm and

r 0.8/1.0 mm. The total amount of aggregate can be seen from table 9.

ngation, flakiness and quantities was calculated for each mix according to the

te combination and mix design.

ntial input values are (denotation in bold):

C – Elng 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm

Elongation of size fractions, 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm), [ - ]

C – Flkn 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm

Flakiness of size fractions, 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm), [ - ]

C – Qnty 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm

Particle quantity of size fractions, 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm), [ - ]

C- Surface area 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm

Surface area of size fractions, 3.15/4.0, 1.6/2.0 and 0.8/1.0 mm), [ - ]

C – tot. surface (total surface, semi-coarse), [ - ]

ngularity/roundness, semi-coarse fractions

ity is determined from the morphological spectrum of the particle and is

ated by performing successive morphological �eroding� operations using disk-

masks. The �eroding� operation rounds the edges of the particles and removes

om the particle, depending on the size of the mask and the ruggedness of the

61

border. The features are computed using masks with radii of 3, 5, 7, 10 and 12 pixels.

The number of pixels eroded from the stone is then divided by the square of the mask

radius. As a result a sequence of five numbers is acquired, representing the shape

distribution of the stone border. An average of the five-number sequence was calculated,

and for the purpose of input parameter, the angularity was additionally averaged

according to the particle quantity of the three size fractions. (GONZALES AND WOODS

1993)

Angularity is computed with regard to each aggregate particle independently. The results

of a given image are then the expectation values of the independent particles.

The potential input value is:

1. SC – Angularity (angularity, semi-coarse), [ - ]

4.4.10 Surface texture, semi-coarse fractions

When the surface texture is determined, a square is fitted inside the aggregate particle

and texture inside the square is considered. Because the size of the texture samples varies

from particle to particle, the texture sample is copied periodically so that samples from

different particles become comparable. A two-dimensional Fourier transform is

computed from the texture sample. In the two-dimensional case the Fourier transform

coefficients are too numerous to be useful alone as features. Instead, radial sums of the

absolute values of the Fourier transform coefficients from [pi/2..pi/4] band is used. The

input parameter was calculated as the weighed average of the particle quantity of the

three particle sizes. (JAIN 1989)

The surface texture is computed with regard to each aggregate particle independently.

The results of a given image are then the expectation values of the independent particles.

The potential input value is:

1. SC - Surface texture, (surface texture, semi-coarse) [ - ]

62

4.5 Testing methods and concrete output values

All concrete castings were made and tests conducted at the Fortum concrete laboratory in

Vantaa, except in the case of the thin-section analyses, which were carried out at the

Finnish Research Centre in Otaniemi.

4.5.1 Workability

For workability determination, the slump and flow value was applied according to testing

methods ISO 4109 and SFS 5286 respectively (figure 14). The slump was measured 5

and 15 minutes and flow values 7 and 17 minutes after the mixing.

Figure 14. The measuring of the slump

and flow value

The outputs are:

1. Slump 5 min, [ mm ]

2. Flow value 7 min, [ mm ]

63

4.5.2 Air % and density of fresh concrete

The testing of the percentage of air in the concrete was performed according to the

standard ISO 4848, as shown in figure 15. The testing was done 12 minutes after the

mixing. Prior to the air % measurement, the 8 litre vessel filled with concrete using

standard compaction routines was weighed, and in this way the unit density of the fresh

concrete was determined.

Figure 15. Measurement of the air % of fresh concrete

The testing method for the air % measures all the air in the concrete: entrapped and

entrained air. Therefore no judgement of the quality of the air can be made on the basis

of this measurement. Consequently, thin-sections were made from the AE castings for

the air parameter determinations (see 4.5.6).

The outputs are:

1. Air %, fresh concrete, [ % ]

2. Density of the fresh concrete, [ kg/m3 ]

3. Excess density of the fresh concrete, [ % ] (deviation from the theoretical density)

64

4.5.3 Bleeding

The measurement of bleeding was done after intervals of 10 min, 30 min and 60 min.

Approximately 2 litres of concrete was put into a plastic cylinder container and vibrated

slightly. The container was covered with a lid and placed in a quiet place to rest. After a

specified time the bleeding water was sucked out and weighed. The bleeding was

calculated by means of the equation:

Bleeding = Vw / V, Equation 12

where

Vw = the amount of bleeding water, [ g ]

V = the volume of the concrete

With some concrete mixes, it was difficult to measure the bleeding water accurately due

to the large overall amount of bleeding and the segregation tendency of the concrete. The

bleeding liquid consisted of both water and very low viscosity cement paste, and hence

the “choice” of the liquid was some times challenging.

The outputs are:

1. Bleeding 10 min, [ g/cm3 ]



4.5.4 Compressive strength and density of the hardened concrete

The compressive strength and density were determined at the ages of 24h, 28d and 91d.

All the values are average for three 100 mm cubes. The density determinations were

made as mass per volume calculations based on weighing the specimen in air and in

water (figure 16).

65

Figure 16. Density determination of the hardened concrete

The 28d and 91d specimens were stored in a climate room with RH >95% and

temperature 20 ± 2 °C. At the testing age the specimens were taken out of the climate

room 3 hours before testing.

The outputs are:

1 – 3. Compressive strength 24h, 28d and 91d, [ MPa ]

4 – 6. Standard deviation of the compressive strength 24h, 28d and 91d, [ MPa ]

7 – 9. Density 24h, 28d and 91d, [ kg/m3 ]

10–12. Standard deviation of the density 24h, 28d and 91d, [ MPa ]

13-15. Excess density 24h, 28d and 91d, [ % ] (deviation from the theoretical density)

66

4.6 Testing methods for drying shrinkage, weight loss and air parameters,

hardened concrete

4.6.1 Drying shrinkage and weight loss

For drying shrinkage and weight loss determinations two 100 x 100 x 500 mm prisms

with measuring bolts in the ends were cast. After demoulding, the prisms were placed in

water for curing for 6 days before weight and length measurements were conducted;

these were set as the zero values for the drying shrinkage and weight loss determinations.

The prisms were stored in a climate room with RH 40 ± 3 % and temperature 20 ± 2 °C.

Weight and length determinations were done at the ages of 14, 21, 35, 49, 63, 91, 119,

147, 175, 203 and 231 days. A general view of the climate room and of the set-up for the

length measurement can be seen in figures 17 and 18.

Figures 17. General view of the drying shrinkage climate room (left)

Figure 18. Set-up for the drying shrinkage measurement (right)

67

4.6.2 Thin section analysis; air %, specific surface area and spacing factor of air void

From all the castings one thin-section pre-sample was made by cutting a 30x50x10 mm

prism vertically from the middle of a 100 mm cube. The pre-samples were impregnated

with clear resin at normal atmosphere pressure, and thus the reactions in the concrete

were interrupted. Final petrographic thin sections were prepared from all the AE castings.

The pre-samples were vacuum impregnated with fluorescent coloured resin and glued on

slides. Finally the samples were diamond cut and grind down to sizes 30x50 mm2 x 25

µm. A detailed description of the preparation method is contained in standard NT Build

381.

The analyses were performed using a Leica DM LP polarisation and fluorescence

microscope, applying the modified point-count method described in standard ASTM

C457 (NT Build 381). Air pores which had diameter < 0.8 mm were counted as air voids

and pores with greater diameter as entrapped air pores. The minimum analysed air pore

size was with diameter 0.020 mm. The analysed parameters were: air void percentage,

entrapped air percentage, specific surface of the air voids (mm2/mm3) and spacing factor

of the air voids (mm).

68

5. AGGREGATE TEST RESULTS AND DISCUSSION

The test programme was constructed in such a way that the fines, < 0.125 mm, and the

semi-coarse fraction, 0.125/4 mm, could be interpreted separately. This is vital. The

reason is that, although the testing methods for these two groups are different, the test

results can correlate quite strongly, and thus one phenomenon can hide the other. As two

examples one may give the results of the F-LD specific surface area for rock products vs.

the SC- Los Angeles value and the F- BET specific surface area for gravel products vs.

the SC- total pore area (figures 19 and 20).

Figures 19 and 20. Correlations between the fines and the semi-coarse fractions;

Examples: F- LD vs. SC- Los Angeles and

F- BET vs. SC-total pore area

It is inevitable that there will be fines and semi-coarse inputs such that the separate fines

inputs will correlate with each other; the same applies to the semi-coarse fraction inputs.

The correlations between the inputs are taken into account in the Excel program for the

prediction of fine aggregate�concrete interaction by Principal Component Analysis (see

chapter 3). Appendix 3 provides lists of the correlations within the fines and semi-coarse

inputs.

LD SSA fines vs Los Angeles value (mod.)A -aggregate

R = 0.91

15

20

25

30

35

40

45

0.2 0.3 0.4 0.5

LD specific surface area [m2/g]

Lo

s A

ng

eles

val

ue [

%]

BET SSA fines vs Total pore area SC B - aggregate

R = 0.97

0

0.1

0.2

0.3

0.4

0 5 10 15BET specific surface area [m2/g]

To

tal p

ore

are

a [m

2 /g]

69

5.1 Mineralogical composition

The X�ray diffraction determinations are only semi-quantitative and thus the actual

quantities are difficult to determine with high precision. The results from the

determinations are presented in table 12-14 with an accuracy of within 1%�unit for the

lower concentrations (<10 %) and 5 %-unit for the higher concentrations (≥ 10%).

Table 12. Mineralogical composition for the rock products; X-ray determination

A2

[%]

F SC

A3

[%]

F SC

A6

[%]

F SC

A7

[%]

F SC

A8

[%]

F SC

A10

[%]

F SC

A15

[%]

F SC

A16

[%]

F SC

Quartz 7 40 40 35 15 30 35 45 10 25 4 15 4

Potassium feldspar 30 30 30 30 15 20 8 9

Plagioclase 35 30 25 35 30 40 35 30 80 60 30 45 75

Hornblende 1 45 6 5 10 65 20 20

Biotite 3 1 1 1 10 6 2 15 8 7 7 4

Chlorite 1 2 1 1 4 3 1 1 3

Carbonate 15

Pyroxene 15

Garnet 2 2

Table 13. Mineralogical composition for the B1-8 gravel products; X-ray determination

B1 [%]

F SC

B2 [%]

F SC

B3 [%]

F SC

B6 [%]

F SC

B7 [%]

F SC

B8 [%]

F SC

Quartz 20 30 35 35 35 15 20 20 35 45

Potassium feldspar 15 25 10 20 25 8 8 20 10 25

Plagioclase 25 30 30 40 35 20 40 50 35 30

Hornblende 4 3 2 3 10 8 5

Biotite 10 2 2

Muscovite 6 10 10 1 3

Chlorite 1 2 10 7 4 7 1

Smectite 20 25 9 5

Kaolinite 15 2

Vermiculite 4 1

Talc 1 2

70

Table 14. Mineralogical composition for the B9-14 gravel products; X-ray determination

B9 [%]

F SC

B10 [%]

F SC

B11 [%]

F SC

B12 [%]

F SC

B13 [%]

F SC

B14 [%]

F SC

Quartz 55 40 35 25 30 25 20 40 35 25

Potassium feldspar 10 20 8 35 10 20 10 20 15 30

Plagioclase 20 30 30 40 35 45 30 30 35 40

Hornblende 2 8 6 3 3 2 5 1

Biotite 3 1 2 1 1

Muscovite 3 15 1 15 2 2

Chlorite 5 6 1 6 3 8 6 3 1

Smectite 5 2 15

Kaolinite 7

Vermiculite 7

Talc 2

The mineralogical composition of the SCF and the fines can vary substantially both with

the rock and the gravel products (tables 12-14). For the rock products, the reasons for the

deviation lie in the heterogeneous of the rock product itself, enriching of minerals during

the crushing process and representativeness of the samples. The gravel products, on the

other hand, have been through weathering processes and hence the mineralogical

composition, especially of the fines, has changed during a long period. The weathering of

the gravel products can indirectly be measured, e.g. using the BET method, and as

general, such products which have larger amounts of clay minerals show also higher

values in the BET determination.

5.2 Grading

The measurements were performed on three test specimens and each specimen being

determined twice. The average coefficient of variation of the surface area (calculated

from the grading information) was 4.6 ± 1.9%.

71

The potential input values i.e. fineness, shape of the grading curve and one passing-%

value (0.008 µm), were chosen so that the grading curve could be described by them and

its orientation would be unambiguous.

Figure 21 illustrates the three potential input values from the grading curve for each of

the fines. The scatter in the values was in general greater for gravel products than for

rock products. Thus, the lowest and highest fineness values for the gravel and crushed

rock products were 338 (B14) � 644 (B11) and 425 (A8) � 636 (A15) respectively. The 8

µm passing-% values varied with the gravel products within the range of 5.9% (B3) and

34.6% (B11), whereas the rock products had values within the range of 13.0% (A8) and

27.9% (A15). Furthermore, the Cu values for the gravel products varied from 3.4% (B3)

to 16.5% (B10) and for the rock products from 10.1% (A8) to 16.2% (A3). The mix

values for the grading inputs were calculated proportional to the percentage of the

different fines.

Figure 21. Values for fineness, 8µm passing-% and Cu for each of the fines

Fineness, 8 µm passing-%, Cu

0

100

200

300

400

500

600

700

800

900

B1

B2

B3

B6

B7

B8

B9

B10

B11

B12

B13

B14 A2

A3

A6

A7

A8

A15

A16

Fines

Fin

enes

s

0

5

10

15

20

25

30

35

408

µm p

-% a

nd

Cu

Fineness

8 µm p-%

Cu

72

Figures 22 and 23 show the grading curves of the finest and coarsest gravel and rock

fines. Figure 22 presents the cumulative grading curve and the figure 23 shows the same

information in differential format.

Figure 22. Cumulative grading curves of A8, A15, B3 and B11 products, fines

Figure 23. Differential grading curves of A8, A15, B3 and B11 products, fines

Cumulative grading curve

0102030405060708090

100

0.01 0.1 1 10 100 1000Particle diameter [µm]

Vo

lum

e [%

]

A08

A15

B03

B11

Differential grading curve

0

2

4

6

8

10

0.01 0.1 1 10 100 1000

Particle diameter [µm]

Vo

lum

e [%

]

A08

A15

B03

B11

73

As can be seen from figure 22, the shape of the cumulative grading curves for the rock

products are very similar to each other. In contrast, the shape of the cumulative grading

curves for the gravel products vary considerably. This could of course be due to the

choice of test material, but more likely, it results from the classification actions caused by

the glacial periods and proceeded weathering. The rock products have been subjected

only to compressive stressing processes, and thus the fines generated fall within narrower

grading limits, (RUMPF 1990). The differential grading curve of the B11 reveals that the

product has a fairly large amount of clay-size particles and thus indicates weathered

material. This can additionally be seen from the SEM pictures, figures 24, of the fines as

well as from their surface area values (see 5.3).

B11

A15

B3

A8

Figure 24. SEM pictures of the fines B3, B11, A8 and A15. Magnification x 500.

74

The SEM pictures also accord well with the fineness and Cu values of the fines. What can

also be seen from the pictures is the shape properties of the fines. The gravel particles

mainly have a cubical shape, whereas the rock particles are often flaky and/or elongated.

The structure, mineral size and mineralogical composition of the rock has a vast

influence on the shape of the particles. The exact amount of the crushed fines was known

but was not used as a potential input, as in the everyday production it would not be

possible to determine it.

5.3 Specific surface area

The LD measurements were made 3*2 times (see 5.2) and the BET measurements three

times. The average coefficient of variation was for the LD 4.6 ± 1.9% and 11.0 ± 9.2%

for the BET.

The specific surface area of the fines was determined by means of two methods: gas

adsorption (BET method) and laser diffraction (LD). Of these two, the gas adsorption

method can be considered a direct method and laser diffraction as an indirect method (see

4.4.2). The BET values of the fines varied between 1.77 m2/g (B3) and 13.98 m2/g (B10)

for the gravel products and between 1.25 m2/g (A8) and 2.73 m2/g (A15) for the rock

products. The corresponding LD values of the fines varied from 0.223 m2/g (B3) to 0.633

m2/g (B11) for the gravel products and from 0.259 m2/g (A8) to 0.682 m2/g (A15) for the

rock products. Figure 25 shows the results obtained from both determinations.

75

Figure 25. Specific surface area values determined using the BET�method and LD

The LD value describes the grading/fineness of the fines as one value. Because the

method calculates the surface area, i.e. constitutes indirect determination, and assumes

the particles to be spheres, it can be concluded that the value does not contain any

information on the particle shape or the weathering properties of the fines. As BET is a

direct method, it contains both types of information, although weathering is more

prominent than the shape properties. In practice, this means that the BET value should be

considered when the water-cement ratio of the mixes is determined with the bone-dry

state of the aggregate products. Consequently, the LD surface area value should be

considered when the w/c ratio is based on the saturated-surface dry (SSD) state of the

aggregate products. Figure 26 shows pictures of the B10 and A7 fines that have close LD

values, 0.444 m2/g and 0.475 m2/g, but very different BET values, 13.98 m2/g and 2.44

m2/g.

Specific surface area

0

2

4

6

8

10

12

14

16

B1

B2

B3

B6

B7

B8

B9

B10

B11

B12

B13

B14 A2

A3

A6

A7

A8

A15

A16

Fines

BE

T [

m2 /g

]

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

LD

[m

2 /g]

BET

LD

76

B10 A7

Figure 26. SEM pictures of the fines B10 and A7. Magnification x 1500

The correlation between LD and BET is non-significant (R = 0.22), but if the gravel and

rock products are interpreted as separate groups the correlation for rock products

becomes significant (R= 0.76). The reason becomes clear if we consider the fact that the

rock products are virtually non-weathered and that the main difference between the LD

and BET results is thus due to the shape properties of the particles.

5.4 Particle density

The average coefficient of measurement variation was 0.12 ± 0.03% and was determined

from 12 aggregate fractions, 9 SCF and 3 fines. The determinations were carried out

with three test specimens and each specimen was measured three times (3 x 3 times).

The remaining 23 aggregate fractions were measured 1x 3 times.

Fines

The particle density values for the fines were quite constant for the gravel products. The

values varied between 2.681 Mg/m3 (B3) and 2.797 Mg/m3 (B7). Greater variation, from

2.654 Mg/m3 (A2) to 3.015 Mg/m3 (A15), was observed in the fines of the rock products

Figure 27 presents the particle density values for each fines.

77

Figure 27. Particle density values for the fines

Semi-coarse fractions

The gravel products showed a small scatter in the particle densities of the semi-coarse

fractions. The lowest value was 2.647 Mg/m3 (B8) and highest 2.726 Mg/m3 (B7). For

rock products, the scatter was greater: between 2.636 Mg/m3 (A3) and 3.008 Mg/m3

(A16). The results are presented in figure 28.

Rock quarries are normally quite heterogeneous when it comes to rock types, and hence

it is possible for particle density to vary significantly from one rock type to another. For

example, a difference of 0.15 Mg/m3 in particle density (2.80 Mg/m3 instead of 2.65

Mg/m3) in normal concrete with 1750 kg of aggregate affects the volume of 1 m3 by �3.6

percentage, i.e. -36 litres/m3.

Particle density

2.4

2.5

2.6

2.7

2.8

2.9

3.0

3.1B

1

B2

B3

B6

B7

B8

B9

B10

B11

B12

B13

B14 A2

A3

A6

A7

A8

A15

A16

Fines

Den

sity

[M

g/m

3]

78

Figure 28. Particle density values for the semi-coarse fractions

5.5 Particle porosity

The measurements were performed 2-6 times for each aggregate product (fines and SCF)

and the average coefficients of measurement variation thus obtained are shown in table

15.

Table 15. Average coefficient of particle porosity measurement variation

Average coefficient of measurement variation

Fines SCF

Average pore size 8.7 ± 9.2% 14.0 ±12.5%

Total pore area 6.9 ± 7.3% 17.7 ± 17.1%

As can be seen from table 15, the accuracy is substantially better for the fines than for the

SCF. This is mostly due to the disproportion between the sample size and the fraction

size i.e. the sample size was 1-2 g and the tested semi-coarse size fraction was 0.5/1.0

Particle density

2.4

2.5

2.6

2.7

2.8

2.9

3.0

3.1

B1

B2

B7

B8

B10

B11

B12

B13

B14

RE

F

A2

A3

A7

A8

A10

A16

Semi-coarse

Den

sity

[m

g/m

3 ]

79

mm. This equals approximately 100 grains. The sample size is same for the fines, which

has size fraction <0.063 mm.

The weathering of the aggregates dissolves minerals, i.e. increases particle porosity,

disintegrates particles and causes mineral transformation. Hence, it is to be expected that

several potential input values will correlate with the particle porosity values. The data

source for the total pore area and average pore size is the same, and thus it is obvious that

they display a good correlation (figure 29) .

Figure 29. Correlations between average pore size and total pore area for the

fines and semi-coarse fractions

Fines

The total pore area and average pore size values for the fines are presented in figure 30.

The gravel products showed greater scatter in the values than did the rock products. For

the total pore area, the gravel products had values between 0.218 m2/g (B14) and 3.574

m2/g (B10), and the rock products showed values between 0.572 m2/g (A6) and 1.031

m2/g (A7). The smallest and largest average pore size values for the gravel and rock

products were 0.459 µm (B10) - 5.230 µm (B14) and 1.420 µm (A7) � 2.514 µm (A8)

respectively.

Avg pore size vs total pore area, fines

y = 1.5078x-1.1469

R = 1.00

0.0

1.0

2.0

3.0

4.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Average pore size [µm]

To

tal p

ore

are

a[m

2 /g

]

Avg pore size vs. total pore area,semi-coarse

y = 0.1404x-0.9831

R = 0.98

0

0.1

0.2

0.3

0.4

0.5

0 2 4 6 8 10 12

Average pore size [µm]

To

tal p

ore

are

a [m

2 /g]

80

Figure 30. Average pore size and total pore area for the fines

A correlation between the total pore area and the BET surface area in the case of fines of

the gravel products is also to be expected, because the molecular area of the nitrogen gas

(16.2 Å2) is much smaller than the smallest pore size diameter (0.006 µm = 60 Å) as

measured by means of mercury intrusion (figure 31). The rock products are not

weathered, and so there is no statistically significance correlation between the total pore

area and the BET value (figure 31).

Figure 31. Correlations between BET value and total pore area, fines

Average pore size and total pore area

0

1

2

3

4

5

6

7B

1

B2

B3

B6

B7

B8

B9

B10

B11

B12

B13

B14 A2

A3

A6

A7

A8

A15

A16

Fines

Avg

po

re s

ize

[µm

]

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

To

tal p

ore

are

a [m

2 /g]

Avg pore size

Total pore area

BET (A) vs total pore area

R = 0.62

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.0 1.5 2.0 2.5 3.0

BET value [m2/g]

To

tal p

ore

are

a [m

2 /g]

BET (B) vs total pore area

R = 0.96

0.0

1.0

2.0

3.0

4.0

5.0

0.0 5.0 10.0 15.0

BET value [m2/g]

To

tal p

ore

are

a [m

2 /g]

81

For powders, i.e. fines, the mercury intrusion method measures not only the particle

porosity but also the pore size distribution and space/voids between the particles (see

4.4.5). The quantity of voids is dependent on the packing degree of the powder. The main

factors affecting the packing degree of the powders are the grading curve, particle shape

and surface adhesion (CUMBERLAND 1987, RUMPF 1990). The Cu value describes the

shape of the grading curve and the linear correlation between the average pore sizes and

the Cu value is 0.91 for the rock products and 0.89 for the gravel products.

Semi-coarse fractions

The highest total pore areas were 0.337 m2/g (B1) and 0.058 m2/g (A7) for the semi-

coarse fractions of the gravel and rock products respectively (figure 32). The lowest

value for the rock products was 0.017 m2/g (A8 and A10) and for the gravel products

0.010 m2/g (B14). The highest average pore sizes were 10.43 µm (A8) and 9.08 µm

(B14) for the rock and gravel products respectively. The lowest values were 0.36 µm

(B1) for the gravel product was and 2.94 µm (A7) for the rock product.

Figure 32. Total pore area and average pore size for the semi-coarse aggregates

Total pore area and average pore size

0

2

4

6

8

10

12

A2

A3

A7

A8

A10

A16 B

1

B2

B7

B8

B12

B13

B14

B15

RE

F

Semi-coarse aggregate

Avg

. p

ore

siz

e [u

m]

0.00

0.06

0.12

0.18

0.24

0.30

0.36

To

t. p

ore

are

a [m

2 /g]

Average pore sizeTotal pore area

82

The total pore area of the semi-coarse fractions is divided into three categories: pore sizes

> 0.09 µm, 0.03�0.09 µm and 0.006�0.03 µm. As can be seen in figure 33, only the

gravel products have pores in the smallest category.

Figure 33. Incremental pore areas for the semi-coarse aggregates

What is noteworthy is that those gravel products that are partly crushed do not contain

the smallest pores and only rock products that have been stored in outdoor stockpiles for

years have the middle-category pores. It is quite likely that for the gravel products, the

crushing process has partly shaken off the most weathered layer, and//or that this layer

has partly flaked off during the crushing. The particle porosity origin from the weathered

layers exist in the aggregate product even after the crushing, however, in smaller particle

sizes. The particle porosity of the B8 product (sandstone) results both from weathering

phenomena and from the sedimentation structure.

The practical influence of aggregate particle porosity on concrete is water absorption.

Normal unweathered aggregate has an absorption capacity of 0.3-0.5% and moderately

weathered aggregate can easily have an absorption capacity of 1.0-1.5% or even higher.

For 1 m3 of concrete with 1750 kg of aggregate, the difference with water absorption

Incremental pore area

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

A2

A3

A7

A8

A10

A16 B1

B2

B7

B8

B11

B12

B13

B14

RE

F


Incr

emen

tal p

ore

are

a [m

2 /g]

>0.09 µm

0.03-0.09 µm

0.006-0.03 µm

83

capacity of 0.7 % (0.3 % → 1.0 %) affects 12 litres/m3 of water when the mix design is

made on a bone-dry basis. If saturated and surface dry basis is used, the influence on

workability is thus insignificant, though the effect on the drying shrinkage still exists (see

chapter 6.5).

5.6 Zeta potential

The zeta potential measurements were made three times for samples without admixture

(N) and additionally three times for samples with superplasticizer (WR). The average

coefficient of variation for the zeta potential measurements was 5.5± 4.0%.

The function of the superplasticizers in concrete is based on adsorption by the cement

particles and the induced effect on the electrical double layer, electrostatic repulsion and

enhanced dispersion of the particles (RAMACHANDRAN 1981, RUMPF 1990, TATTERSALL

ET AL. 1983). The same phenomena are also involved in the way which the

superplasticizer affects the aggregate fines. The higher the absolute value of the zeta

potential, the greater is the particle dispersion. Figure 34 presents the zeta potential

values measured for each aggregate without (N) and with the ionic surfactant, i.e.

superplasticizer Mighty 150. The zeta potential measurements performed with the air-

entraining agent showed no difference in values when compared to N (=no admixture)

levels. Hence, it was concluded that the air-entraining agent develops a lipid layer or

equivalent on the aggregate surface and thus, does not affect the surface electrical

stability.

The range for the fines of the gravel products varied from �9.1 mV (B2) to �2.8 mV

(B10) without the superplasticizer and from �18.3 mV (B14) to �5.7 mV (B10) with the

superplasticizer. For the fines of the rock products the values were between �11.2 mV

(A2) and �5.0 mV (A6) without the surfactant and �17.1 mV (A3) and �12.2 mV (A15)

with the surfactant.

84

Figure 34. Zeta potential values for each fines without and with superplasticizer

As can be seen, the addition of the superplasticizer causes a level change in the zeta

potential value. The regression equations for the level change are as follows:

Zeta change N→ WR (A) = - 746F-LD � 73F- avg. pore size +151F-BET Equation 13

Zeta change N→ WR (B) = - 142F-LD � 49F-avg. pore size Equation 14

According to the equations, the change in the zeta potential value is greater when the

average pore size increases, i.e. when the total pore area is lower. Additionally, the

increase in total fineness also increases the zeta potential change. The equation for rock

products also includes the effect of the BET value, i.e. surface area caused by shape

properties. The greater the change in the zeta potential, thus lower the consumption of

superplasticizer for adequate dispersion of the fines.

The correlation between the measured and calculated values is 0.90 if all the A and B

aggregates are included. If the B9 is omitted the correlation rises to 0.96 (figure 35 and

table 16).

Zeta potential

0

2

4

6

8

10

12

14

16

18

20

B1

B2

B3

B6

B7

B8

B9

B10

B11

B12

B13

B14 A2

A3

A6

A7

A8

A15

A16

Fines

Zet

a p

ote

nti

al [

mV

]WR

N & AE

85

Table 16. The measured and calculated values of the percentile change in

zeta potential of the fines

Figure 35. The correlation of the measured and

calculated zeta potential change-%

5.7 Resistance to fragmentation

The Los Angeles values of the semi-coarse fractions (SCF) are results of one

determination. According to the EN 1097-2 standard the reproducibility of the test is R =

0.17X (X represents the Los Angeles value).

Figure 36 presents the results of the Los Angeles test done on the SCF. As can be seen

from the figure, the gravel products (except the B8) had fairly constant values: between

21.5% (B2) and 29.4% (B12); the B8 had the value of 32.5%. The rock type of the B8

aggregate is sandstone, while all the other gravel products are of granitic origin. By

contrast, rock products showed a large scatter in the results. The A7 had the lowest value,

19.6%, and the A8 had the highest value, 38.2%.

Measured change [%]

Calculated change [%]

B1 -76 -71B2 -66 -94B3 -243 -227B6 -102 -139B7 -145 -123B8 -98 -106B9 -100 -196B10 -104 -86B11 -114 -114B12 -133 -109B13 -142 -115B14 -291 -291A2 -37 -30A3 -159 -139A6 -210 -194A7 -69 -90A8 -172 -188A15 -230 -224A16 -163 -177

Zeta potential Change N->WR vs. Calculated Change N->WR

without B9

R = 0.96

-300

-200

-100

0-300 -200 -100 0

Zeta pot. change [%]

Cal

lc. Z

eta

po

t. ch

ang

e [%

]

86

Figure 36. Results of the Los Angeles test for the semi-coarse aggregates

As the tested samples of the aggregate products were not modified according to shape

(e.g. bar sieved), the results show the true resistance to fragmentation and hence, also

best describe performance in concrete castings.

5.8 Elongation, flakiness, particle volume and quantity

The number of aggregate particles scanned in one image varied according to the particle

size. For the size 3.15/4.0 mm the number varied between 100-200 and for the 1.6/2.0

mm and 0.8/1.0 mm sizes between 400-650 and 500-1200 respectively. The coefficient

of measurement variation, as calculated from area determination, between images of two

sets of particles from same aggregate product was < 5 % in each size fraction.

The accuracy of the calculated particle quantity in the mix design is additionally

influenced by the simplification of the particle shape. All the particles were assumed to

Los Angeles -test (mod.)

15

20

25

30

35

40B

1

B2

B7

B8

B11

B12

B13

B14

RE

F

A2

A3

A7

A8

A10

A16


Lo

s A

ng

eles

val

ue

[%]

87

be spheres. This is not the case, but it allows us to perceive the quantity differences

caused by the shape properties, i.e. average volume scatter.

Figure 37 presents the elongation and flakiness for all three size fractions: 3.15/4.0 mm,

1.6/2.0 mm and 0.8/1.0 mm. Though the values are dimensionless, the information that

they give is the extent to which the dimensions of the particles deviate from the

dimensions of a sphere.

The rock products are more elongated and flaky than the gravel products. The figures

show clearly that the particles of smaller size are more elongated than those of larger

sizes. For the rock products the tendency is seen in the case of all three size fractions, but

with the gravel products the elongation values for the 1.6/2.0 mm and 3.15/4.0 mm

particles are quite close to each other. The B7, B12 and B13 aggregates are party crushed

gravel products and hence have increased elongation and/or flakiness values. In contrast

to elongation, the relative flakiness levels are the same for all size fractions.

Figure 37. Elongation and flakiness values for the three size fractions

Figure 38 shows scanned images of the A7 and REF aggregates. The figure makes

visible the elongation variations in the size fraction 0.8/1.0 mm. The A7 aggregate has

the highest value, 1.64, and the REF aggregate has the lowest value, 1.45.

Elongation

1.30

1.35

1.40

1.45

1.50

1.55

1.60

1.65

1.70

B1

B2

B7

B8

B11

B12

B13

B14

RE

F

A2

A3

A7

A8

A10

A16


Elo

ng

atio

n

0.8/1.0

1.6/2.0

3.15/4.0

Flakiness

1.20

1.25

1.30

1.35

1.40

1.45

1.50

1.55

1.60

B1

B2

B7

B8

B11

B12

B13

B14

RE

F

A2

A3

A7

A8

A10

A16


Fla

kin

ess

0.8/1.0

1.6/2.0

3.15/4.0

88

A7 0.8/1.0 mm

REF 0.8/1.0 mm

Figure 38. Scanned images of the A7 and REF 0.8/1.0 size fractions

The impression one gets from the images is that the A7 particles are bigger than the REF

particles. The 2D area is, however, greater for the A7 than for the REF particles as the

1.0 mm sieve, which has been used for preparation of the narrow size fraction, allows

elongated particles to pass but not those with a width greater than 1.0 mm. Due to the

elongation, the density of the A7 particles (pieces/cm2) is smaller in the image, and this

also strengthens the impression given.

The particle volume calculations have been made partly from a data source different from

that for the elongation and flakiness values, though the particles used for all the

measurements were the same. As can be seen from figures 39 and 37, the particle volume

has the lowest values when the elongation is low and the flakiness high. The following

regression equations can be calculated for the three size fractions:

Volume (3.15/4.0 mm) = 76E - 35F � 18(E*F) Equation 15

Volume (1.6/2.0 mm) = 10.1E - 5.1F - 2.1(E*F) Equation 16

Volume (0.8/1.0 mm) = 1.29E - 0.62F - 0.28(E*F) Equation 17

where E = elongation value and F = flakiness value

89

Figure 39. Particle volume for the three size fractions

The equations are well in line with each other, when the theoretical volume ratios

between the size fractions are taken into account. The 3.15/4.0 mm particles have a

volume 23 * 23 times larger than the 0.8/1.0 mm particles and 23 times larger than the

1.6/2.0 mm particles. The correlation between all three equations and the measured

particle volumes is 1.00.

The quantity data is calculated from the volume data and the quantities are dependent on

mix design, i.e. aggregate volume amount. Table 17 shows the quantities for all

aggregate products with the N35 mix design.

The quantity difference between the �most sphere� and �least sphere� aggregate

products, B1 and A8 respectively, is over 20% in each size fraction. What is also

important to notice is that B7, which is a gravel product containing some crushed gravel,

has more particles than two totally crushed rock products, A2 and A3. We can thus say

that, at least when it comes to paste consumption due to shape properties, unprofessional

production and blending of partly crushed gravel with uncrushed gravel can spoil a good

raw material.

Particle volume

0.40

0.42

0.44

0.46

0.48

0.50

0.52

0.54

0.56

0.58

0.60

B1

B2

B7

B8

B11

B12

B13

B14

RE

F A2

A3

A7

A8

A10

A16


Vo

lum

e 0.

8/1.

0 [m

m3 ]

17

19

21

23

25

27

29

31

Vo

lum

e 3.

15/4

.0 +

(1.

6/2.

0)*6

[mm

3 ]

0.8/1.0

(1.6/2.0)*6

3.15/4.0

90

Table 17. Particle quantities for the N35 mix design

5.9 Angularity and surface texture

The angularity and surface texture determinations for the SCF are based on the same

scanned images used in the case of elongation and flakiness. The coefficient of

measurement variation was < 4 % for surface texture and <5% for angularity.

The surface texture and angularity values are presented in figure 40. As can be seen, the

angularity values are higher for the rock products than for the gravel products. Only the

partly crushed gravel products B7 and B12 have somewhat higher values than the

average gravel products. The highest and lowest angularity values for the gravel and rock

products are 4.9 (B2) - 6.5 (B12) and 7.1 (A2) � 8.7 (A10) respectively (figure 41).

QNTY QNTY QNTY3.15/4.0 mm 1.6/2.0 mm 0.8/1.0 mm

B1 2953585 15062027 90899627

B2 3021109 15948490 92278260

B7 3230442 15217343 98678573

B8 3025736 15882836 92628474

B11 2980233 15802213 92950287

B12 3037531 15136040 97321200

B13 3126541 15909456 96287402

B14 2976494 15018694 92597402

REF 2924470 15036257 91608749

A2 3217892 16103993 97301145

A3 3168835 15456489 93983455

A7 3433438 17197190 100209083

A8 3881904 17194139 114637234

A10 3521490 17023814 107781461

A16 3318100 15756364 102031485

91

Figure 40. Surface texture and angularity values of the semi-coarse aggregates

For the surface texture, there is no gap between the values of the crushed and uncrushed

products. Those gravel products which are either crushed or weathered (high particle

surface porosity) have higher surface texture values than the other gravel products.

Among the rock products, the mineral size and mineralogical composition have the main

effect on the surface texture. The A2 and A3 products are rich with plagioclase, feldspar

and quartz, and their mineral size is fairly large, approximately 3 mm. The A8 and A10

products also have a large mineral size, approximately 3 mm, while the A7 and A16 have

a mineral size smaller than 1mm. The highest and lowest surface texture values for the

gravel and rock products are 0.97 (REF) - 1.36 (B7) and 1.01 (A3) � 1.80 (A16)

respectively (figure 42).

Surface texture and Angularity

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0B

1

B2

B7

B8

B11

B12

B13

B14

RE

F

A2

A3

A7

A8

A10

A16


Su

rfac

e te

xtu

re

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

An

gu

lari

ty

Surface texture

Angularity

92

Figure 41. Scanned images of B2 and A10 with low and high angularity, SCF

A2 and A16

B14 and B1Figure 42. Scanned images of A2, A16, B14 and B1 representing different

surface texture values, SCF

93

The term �surface texture� describes the unevenness of the particle surface. In the case of

the uncrushed gravel products, the surface texture actually characterises how much

dissolving and/or disintegration of minerals has occurred; for the rock products, it

describes the origin of surface roughness on the basis of the mineral and mineralogical

properties, e.g. mineral size and crystal form. The correlation between the total pore area

and surface texture is R = 0.98 for the gravel products (figure 43). For this correlation,

the gravel products B7 and B12 are excluded, because they contain an undetermined

amount of crushed gravel. For rock products the correlation is R = 0.72, and the

correlation covering both the gravel and rock products is R = 0.15.

Figure 43. Correlation between surface texture and total pore area for the gravel products

5.10 Discussion of the test results for aggregates

The fine aggregate products produced from natural aggregate can be divided mainly into

four categories as follows (the three first were studied in this work):

• Uncrushed gravel products

• Crushed rock products

• Partly crushed gravel products

• Mixture of uncrushed gravel and rock products

Surface texture (B) vs. total pore areaB7 and B12 excluded

R = 0.98

0

0.1

0.2

0.3

0.4

0.9 1 1.1 1.2 1.3 1.4 1.5

Surface texture

Tota

l por

e ar

ea [m

2 /g]

94

The test results indicate that some phenomena are more likely to be associated with

gravel products and others with rock products. The partly crushed gravel products and

the mixture of uncrushed gravel and rock products are combinations of the first two

product types. Table 18 presents a general overview of the potential connection between

product type and the quality characteristics.

Table 18. General overview of the potential association between

product type and quality characteristics

SEMI-COARSE FRACTIONS FINES

SHAPE WEATHERING STRENGTH DENSITY WEATHERING FINENESS

UNCRUSHED

GRAVELX X X

CRUSHED

ROCKX X X X

PARTLY

CRUSHED GRAVELX X X X

MIXTURE OF

UNCRUSHED GRAVEL

AND ROCK

X X X X X X

Semi–coarse fractions

Shape: elongation, flakiness, particle volume & quantity and angularity

Weathering: total pore area, incremental pore area, average pore size

Strength: resistance to fragmentation (Los Angeles value)

Density: particle density

Fines

Weathering: BET value, total pore area, average pore size

Fineness: LD value, Cu, 8 µm passing-%, fineness

The rock products are always more elongated, flakier and more angular than the gravel

products. With professional production, the differences in the elongation, flakiness and,

to some extend, also the angularity between the rock and uncrushed gravel products can

be minimised and controlled. However, each rock material has its own characteristic

tendency for good or poor shape properties.

95

Weathering of the gravel is caused by a long period of physical and chemical strain. The

degree of weathering can vary substantially from one gravel product to another. In the

contrast, rock is not normally weathered, excluding the very surface of rock formations.

Particle porosity by mercury intrusion has been used as a measure of the existing degree

of weathering, but it should not be considered an evaluation of the future durability of the

aggregate products.

The variation in the resistance to fragmentation in the granitic gravel was found to be

quite small. One of the major reasons for this is the glacial period, which was so harsh

that only materials of a relatively good strength survived the abrasion effect.

Additionally, the glacial period mixed the different rock types together so extensively

that the variation in strength, as well as the particle density variation in the gravel, is fairy

small. In contrast, the variation in the rock products for both the strength and particle

density can be very large.

Weathering of the fines can be also possible for the crushed rock products, if the rock has

many crack joints where weathering has occurred and if this material then ends up in the

crushed rock products.

The dispersion effect of specific superplasticizer dosage varies appreciably with both the

gravel and rock products. The dominating characteristics for the zeta potential value are

the average pore size and the calculated surface area, and for rock products also the shape

of the fines particles, as this influences the surface area.

96

6. CONCRETE TEST RESULTS AND DISCUSSION

6.1 Workability

The workability of the castings was measured by means of the flow and slump methods.

At the time when the slump value is measured, the concrete is not moving. Thus, it can

be expected that the effective average shear rate will be zero and that the slump value

will correlate only with yield value. In the case of the flow method, the result is to some

extent influenced by the plastic viscosity, though the correlation with the slump value is

reported to be 0.92 (TATTERSALL 1983). In this work, the correlation between the slump

and flow values varied between 0.93 and 0.99 with the N, AE and WR30 castings (figure

44).

Figure 44. Correlation between the slump and flow values

The linear correlation equation varied between

Flow value (N30,AE30,WR30) = [1.4�1.6] * Slump value + [295�275] mm Eq 18

Flow value (N35, AE35) = [1.0�1.1] * Slump value + [330�320] mm Eq 19

Slump vs. Flow

y = 2.5x + 80R = 0.95

y = 1.0 ...1.6x + 330...275R = 0.96

0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300

Slump [mm]

Flo

w [

mm

]

N30 N35 WR30 WR35 AE30 AE35

97

Many of the castings made with the WR35 mix design behaved differently, -more

viscously - partly due to segregation and partly due to excess paste. Consequently, while

the correlation equation differs significantly (Eq 15), the correlation between the slump

and flow values, 0.95, is good.

Flow value (WR35) = 2.5 * Slump value + 80 mm Eq 20

The repetitions of the castings showed that accuracy is better in the case of flow value

than in that of slump. The repeatability errors for the slump and flow values were 12 (10�

16) mm and 15 (13�19) mm respectively. The evaluation of the data was therefore

conducted using the flow results. Table 19 presents the flow value repeatability errors

and group average for N, WR and AE mixes, and figure 45 shows the average, minimum,

maximum and standard deviation values for each mix design.

Table 19. Flow repeatability error and group average

median of 90% quantile (10%-90% quantiles for the median)

N WR AE All

Repeatability error[mm] 21

(16-31)

9

(6-13)

10

(6-19)

15

(13-19)

Group average [mm] 70

(62-89)

128

(122-139)

61

(48-72)

104

(97-119)

The repeatability error is smallest for the WR mix designs and highest for the N mix

designs. The use of admixtures with normal dosages improves the cohesion of concrete

and thus advances congruent behaviour in the workability repetitions. Nevertheless, in

the case of the WR mix designs, the result scatter is the largest. The WR30 mix design in

particular shows a large result range, 295-590 mm, which demonstrates that

small/moderate changes in aggregate parameters can have a strong influence on concrete.

For each mix design group the result scatter is smaller for the high paste than the low

paste mixes. When the repeatability errors and group averages are compared, it can be

stated that for the N mix designs less than 30%, for the AE mix designs less than 16%

and for the WR mix designs less than 7% of the difference can be explained by the

98

repeatability error. When all the mix designs are taken into account, the repeatability

error explains 14% of the result scatter.

Figure 45. Flow value statistics for each mix design

Figure 46 presents measured flow table values for six WR30 and WR35 castings.

Figure 46. Measured flow table value for different combinations of fines and SCF (The repeatability error is indicated by bars on the columns)

Flow value, 5 min

250

350

450

550

650

750

B1/B1 B3/B1 B1/REF B3/REF B3/A8 B13/B13

Fines/Semi-coarse combination

Flo

w v

alu

e [

mm

]

WR30

WR35

Flow 5min

108344469973758= Stdev

200

300

400

500

600

700

800

Flo

w [

mm

]

avg [mm] 407 505 432 645 330 442 460

max 495 570 590 730 435 505 730

min 290 420 295 525 250 345 250

N30 N35 WR30 WR35 AE30 AE35 All

99

Figure 46 makes it clear that the characteristics of both the fines and the SCF affect the

flow value. The main aggregate characteristics in the castings are shown in table 20.

Table 20. Main aggregate characteristics for the WR castings presented in figure 46

Aggregate Fines Semi-coarse fraction

B1 High surface area High particle porosity

Good shapeB3 Low surface area

REF Low particle porosity

Good shapeA8 Low particle porosity

Poor shapeB13 Medium surface area Medium particle porosity

Fair shape

Shape of the semi-coarse fraction

The effect of poor semi-coarse fraction shape prevails when the amount of paste is low,

but when enough paste is available, the shape characteristics can be mostly overcome, as

can be seen from the B3/A8 castings. The influence of the shape characteristic (flakiness

3.15/4.0 mm) on the workability of the WR 30 and WR35 castings can also be seen from

figure 47. The difference between the mix designs WR30 and WR35 consists of 45

litres/m3 of paste, including 29 litres of water and 50 kg of cement.

Figure 47. Correlations between flow value and flakiness for the WR castings

Flow (WR30) vs. Flakiness 3.15/4.0 mm

R = 0.73

250

350

450

550

650

1.200 1.250 1.300 1.350 1.400 1.450

Flakiness 3.15/4.0 mm

Flo

w v

alu

e [m

m]

Flow (WR35) vs. Flakiness 3.15/4.0 mm

R = 0.08

450

550

650

750

1.200 1.250 1.300 1.350 1.400 1.450

Flakiness 3.15/4.0 mm

Flo

w v

alu

e [m

m]

100

Particle porosity of the semi-coarse fraction and surface area of the fines

Both the particle porosity of the semi-coarse fraction and surface area of the fines have a

clear effect on workability, mainly through water absorption. As one workability class is

60 mm in flow value (EN206), thus the effects of the fines surface area and SCF particle

porosity can be calculated into changes in flow classes (table 21).

Table 21. Changes in flow classes due to the surface area of the fines and pore area

of the SCF; WR30 and WR35 mix designs

WR30

Flow class change

WR35

Flow class change

Effect of fines surface area

when SC particle porosity is high

(∆ = 80 mm)

≈ 1.3 classes

(∆ = 50 mm)

≈ 0.8 classesEffect of fines surface area

when SC particle porosity is low

(∆ = 120 mm)

≈ 2.0 classes

(∆ = 45 mm)

≈ 0.8 classesEffect of SC particle porosity

when fines surface area is high

(∆ = 140 mm)

≈ 2.3 classes

(∆ = 145 mm)

≈ 2.4 classesEffect of SC particle porostiy

when fines surface area is low

(∆ = 165 mm)

≈ 2.8 classes

(∆ = 135 mm)

≈ 2.3 classesEffect of both

SC p.porosity and fines surface area

when both change from high to low

(∆ = 270 mm)

≈ 4.5 classes

(∆ = 190 mm)

≈ 3.2 classes

The separate effects of the fines and SCF can best be evaluated using the WR35 castings,

as the amount of paste and water are higher than those of the WR30 castings, and thus,

the absorption caused by the fines and SCF does not lead to friction between aggregate

particles and to decreased workability. The particle porosity of the SCF has a greater

effect on the workability than does the surface area of the fines. As can be seen in table

21, the effect of the particle porosity of the SCF on the WR35 flow values is three times

greater than that of the surface area of the fines.

The cumulative effect of the fines and SCF is more than three flow classes, if they both

change from low to high. On the other hand, because of the low surface area of the fines,

101

the cohesion of the B3/REF concrete was so low that the concrete became segregated and

thus would not be suitable for concrete production.

Medium values for surface area (fines), particle porosity and shape (SCF)

The SCF B13 is a partly crushed gravel product and thus has a somewhat worse shape

than the B1 and REF products. However, the shape is far better than it is for the A8.

Additionally, the particle porosity of the SCF B13 falls between the A8-REF and B1.

Furthermore, the surface area of the B13 fines also lies between those of the B1 and B3.

The results for the B13/B13 castings are in line with the aggregate characteristics. Hence,

the B13/B13 castings equal the B3/B1 and B1/REF castings respectively, thus

representing average aggregate quality.

6.2 Air %

6.2.1 Air %, fresh concrete

The air in the concrete can be divided into two groups: intentionally entrained air (AE

mix designs) and air entrapped because of unsuccessful compaction/low degree of (N and

WR mix designs). The concrete mix designs were calculated inclusive of the air. For the

AE mix designs the target value was 5.0%, and for the N and WR mix designs it was

1.0%. For the N35 and WR35 mixes the average values are close the target value;

deviations + 0.1%-unit and � 0.3%-unit respectively. However, for the N30 and WR30

mix designs the deviations are greater than the repeatability error (figure 48 and table

22). The AE mixes have a large variation in the air % values. For the AE30 mixes, even

the average value is below the target value and the difference between the minimum and

maximum values is more than 200%, i.e. 2.1% and 6.6%. For the AE35 mixes the

average is within the target range, but the difference between the minimum and

maximum values is more than 100%, i.e. 3.0% and 7.5%.

102

Figure 48. Air %, fresh concrete statistics for each mix design

As the mix design calculations were performed using the target air % values it was

therefore to be expected that the actual densities of the castings would deviate according

to the difference between the actual and target air %. Figure 49 shows the excess density

percentage for the fresh concrete and additionally the excess densities of the hardened

concrete at the ages 24 h, 28d and 91d. The upper set of data is the for the AE mix

designs and the lower set is for the N and WR mix designs.

As can be seen from figure 49, the excess density of the fresh concrete accords quite well

with the calculated density of the concrete inclusive the 1.0% and 5.0% of air. The

deviation, which is approximately 0.5%-unit, is caused by the water absorption of the

aggregate. Additionally, figure 49 demonstrates how the density of the concrete increases

along the degree of hydration (see 24h cubes vs. 91d cubes). The difference between the

densities of the 28d and 91d cubes is within the measurement accuracy. What is

Air % , fresh concrete

1.80.90 .90 .40 .40 .30.2=Stdev

0

1

2

3

4

5

6

7

8

Air

%

avg [%] 1 .7 1 .1 1 .5 0 .7 3 .9 5 .4 2 .4

m ax 2.1 1.6 2 .5 1 .5 6 .6 7 .5 7 .5

m in 1.0 0.5 0 .7 0 .2 2 .1 3 .0 0 .2

N30 N35 W R30 W R35 AE30 AE35 All

103

noteworthy is that the excess density % scatter for the AE mix designs is much greater in

the case of cubes than fresh concrete. This indirectly indicates that the air structure has

not been stable in all the mixes.

Figure 49. Excess density % of fresh concrete and 24h, 28d and 91d cubes

For the N mix designs the scatter of the results and the measurement accuracy are such

that the expectation values of the group average model and repeatability error are

virtually the same. For the WR mix designs the group average is 8 times greater than the

repeatability error, and for the AE mix designs the repeatability error represents one third

of the group average value (table 22).

Excess density % vs. air %, fresh concrete

0

1

2

3

4

5

6

7

8

-3 -2 -1 0 1 2 3 4 5Excess density [%]

Air

% o

f co

ncre

te

Excess density% vs. air %, 24 h cubes

0

1

2

3

4

5

6

7

8

-1 0 1 2 3 4 5 6 7

Excess density [%]

Air

% o

f co

ncr

ete

Excess density% vs. air %, 28d cubes

012345678

-1 0 1 2 3 4 5 6 7Excess density [%]

Air

% o

f con

cret

e

Excess density% vs. air%, 91d cubes

012345678

-1 0 1 2 3 4 5 6 7Excess density [%]

Air

% o

f co

ncr

ete

104

Table 22. Air % repeatability error and group average


N WR AE All

Repeatability error[%] 0.3

(0.2�0.4)

0.1

(0.1-0.2)

0.4

(0.3-0.8)

0.3

(0.2-0.3)

Group Average [%] 0.4

(0.4-0.5)

0.8

(0.6-0.8)

1.5

(1.1-2.2)

0.9

(0.8-1.0)

For the N and WR mix designs, a strong inverse correlation, R = -0.91, between the

entrapped air and flow value can be detected, i.e. when the workability is poor, the

normal compaction energy is not sufficient to force the air out of the concrete (figure 50).

The reverse phenomenon applies to the AE mix designs, R = 0.89 and R = 0.52 for the

AE30 and AE35 respectively. When the workability is good, the entrained air % also

tends to be higher (figure 50).

Figure 50. Correlation between entrapped and entrained air % and flow values

As the correlations are so strong, we can conclude that fine aggregate characteristics that

affect the workability also affect the entrapped air %. When the amount of paste is low,

the entrained air % is also influenced by aggregate characteristics that affect the

workability. With higher amounts of paste, other mix design and/or aggregate

characteristics begin to compensate for the influence.

Air % (N & WR) vs. Flow 5 min

R = -0.91

250

450

650

850

0 0.5 1 1.5 2 2.5 3

Air %, fresh concrete

Flo

w [m

m]

Air % (AE) vs. Flow 5 min

R = 0.89

R = 0.52

250

350

450

550

0 2 4 6 8


Flo

w [

mm

]

AE30

AE35

105

6.2.2 Air %, hardened concrete

Thin sections of all the AE castings were made for air analysis. The factors determined

were: the entrained and entrapped air %, the specific surface of the entrained air voids,

and the spacing factor, i.e. the thickness of the hardened cement paste between adjacent

air voids. Tables 23 and 24 present the thin-section statistics for the AE30 and AE35 mix

designs. Additionally, for comparison purposes, the tables show the air % statistics, with

the volumetric method from the fresh concrete.

Table 23. Thin-section statistics from the AE30 mix design

Table 24. Thin-section statistics from the AE35 mix design

As can be seen from tables 23 and 24, the AE30 castings are more homogenous with the

air quality. Furthermore, the deviations between the volumetric air % and thin-section air

% are smaller for the AE30 than for the AE35 castings.

These results reveal that the repeatability of the thin-section is two sided. The results

were relatively constant if the air void system was stable and if not, they could deviate

strongly from each other (table 25 and figures 51). The repeatability error for the

volumetric method is covered in chapter 6.2.

AE30

Entrained air

[%]

Entrapped air

[%]

Total air [%] Specific surface of

voids [mm2]

Spacing

Factor

Volumetric

method

AE30

Total air [%]

avg 2.6 1.5 4.1 44 0.16 avg 3.9stdev 1.0 0.9 1.3 10 0.05 stdev 0.9max 6.4 4.1 7.2 67 0.29 max 6.6min 1.1 0.2 2.5 27 0.10 min 2.1

AE35

Entrained air

[%]

Entrapped air

[%]


voids [mm2]

Spacing

Factor

Volumetric

method

AE35

Total air [%]avg 3.2 1.5 4.7 42 0.20 avg 5.4

stdev 1.3 1.1 1.8 11 0.20 stdev 0.9max 5.7 3.6 8.1 60 1.07 max 7.5min 0.3 0.0 0.6 15 0.11 min 3.0

106

Table 25. Examples of thin-section analysis results from repeated castings

Casting # 64 Casting # 58

Figure 51. Pictures of repetition castings of the same mix with unstable

void structure.

The castings had very similar fresh concrete values;

air 6.0% and 5.6%, flow value 440 mm and 435 mm,

bleeding 60 min 1.0 g/cm3 and 1.1 g/cm3

for castings #58 and #64 respectively.

Recipe #Entrained air

[%]

Entrapped air

[%]


voids [mm2]

Spacing

Factor

AE35: B9 (50%)+ A3 (50%) / B7 (50%) + A2 (50%)26 4.3 0.7 5.0 43 0.1160 3.8 0.2 4.0 47 0.1166 4.5 0.2 4.7 35 0.1489 3.5 2.1 5.6 46 0.12140 3.0 1.5 4.5 58 0.11

AE35: B7 (100%) / B7 (100%)58 0.5 3 3.5 15 0.8564 4.3 3.2 7.5 43 0.13

107

6.3 Bleeding

The bleeding was determined at three time intervals: 10, 30, 60 minutes after the mixing

was completed. Normally the bleeding proceeds at a constant rate, and water appears on

the surface as the mix constituents settle downwards. However, if the concrete

segregates, the water comes to the surface directly after mixing. These phenomena can be

seen from tables 26 and 27 and figure 52, which present the 10 min, 30 min and 60 min

bleeding statistics for the six mix designs. Table 28 shows the bleeding 60 min

repeatability error and group average confidence limits with quantiles for the N, WR and

AE mix designs.

Table 26. Bleeding 10 minutes, statistics for each mix design

10 min [g/cm3] N30 N35 WR30 WR35 AE30 AE35 All

Average 0.0 0.0 0.0 1.2 0.0 0.0 0.4

Stdev 0.1 0.1 0.0 3.0 0.0 0.4 2.7

Max 0.3 0.3 0.0 13.6 0.0 1.0 25.5

Min 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Table 27. Bleeding 30 minutes, statistics for each mix design

30 min [g/cm3] N30 N35 WR30 WR35 AE30 AE35 All

Average 1.1 1.8 0.1 2.3 0.3 0.4 1.4

Stdev 0.6 0.8 0.3 3.8 0.3 0.2 4.4

Max 2.9 4.2 1.5 13.3 0.9 1.0 44.1

Min 0.0 0.2 0.0 0.0 0.0 0.0 0.2

Table 28. Bleeding test repeatability error and group average


N WR AE All

Repeatability error [g/cm3] 1.1

(0.8-3.2)

2.2

(1.5-4.0)

0.3

(0.2-0.6)

1.3

(1.1-1.7)

Group Average [g/cm3] 2.5

(2.3-3.2)

3.6

(2.2-7.8)

0.9

(0.6-1.2)

2.3

(2.0-2.5)

108

Though the expectation values of the repeatability errors are smaller than the group

average, the quantiles for the repeatability median are large, and thus the measurement

error can actually even exceed the bleeding estimation based on the group average (N

and WR mix designs). The determination of what was bleeding water and what was grout

(cement+water) from the segregated castings was difficult and has thus increased the

repeatability error. Results for two castings were even disregarded for this reason (values

for bleeding 60 min; 47.9 and 58.2 g/cm3).

Figure 52. Bleeding 60 min statistics for each mix design

Both the air-entraining agent and superplasticizer decrease bleeding in comparison with

no-admixture concrete. An exception occurs when the superplasticizer is used with

concrete of very high workability. In this case, the bleeding increased substantially due to

the segregation (figure 53). This phenomenon involved some WR35 castings, where the

water absorption characteristics of the aggregate composition, the particle porosity of the

SCF and the BET value of the fines were small/low. As can be seen, the correlation is

significant for all mix designs but is highest for the WR35 mix design.

Bleeding 60 min

1.3=s tde v

1.8 0.5 4.8 0.5 0.6 2.6

0

4

8

12

16

20

Ble

edin

g [

g/c

m3 ]

avg [g /cm 3] 3.2 4.9 0.6 7.6 1.1 1.5 2.6

m ax 6.8 9.6 2.5 18.5 2.6 3.0 18.5

m in 0.8 0.9 0.1 0.3 0.4 0.2 0.1


109

Figure 53. Correlation between flow value and bleeding

The dividing line between the bleeding and segregation is blurred, and what is typical of

bleeding is that when the threshold for the ability to hold the mixing water is passed the

bleeding tendency increases rapidly. This can be observed from figures 54 and 55, which

show the correlations between F-BET value and bleeding for the N30 mix design, and

between SC- pore area >900Å and bleeding for the AE30 mix design.

Figure 54. Correlation between F- BET value and bleeding; N30

Figure 55. Correlation between SC- pore area >900Å and bleeding; AE30

BET (N30) vs. Bleeding 60 min

R = -0.60

0

2

4

6

8

0 5 10 15

BET value [m2/g]

Ble

edin

g [

g/c

m3 ]

Pore area S-C, >0.09 µm, (AE30) vs. Bleeding 60 min

R = -0.77

0

1

2

3

0 0.05 0.1 0.15

Pore Area >0.09 µm [m2/g]

Ble

edin

g [

g/c

m3 ]

Flow value (WR35) vs. Bleeding 60 min

R = 0.80

0

4

8

12

16

20

500 550 600 650 700 750

Flow value [mm]

Ble

edin

g [

g/c

m3]

N30 0.46N35 0.58

WR30 0.44WR35 0.80AE30 0.44AE35 0.38

Correlation for each mix design

110

Both the F- BET value and the SC- pore area are characteristics that describe the

absorption tendencies and also the water-retaining capacity of the aggregate. If the

aggregate combination has a low value for both these aspects, bleeding will occur. The

amount is dependent on the mix design and admixture combination.

6.4 Compressive strength

The compressive strength was determined at three ages, 1d, 28d and 91d. For each

casting and age, the compressive strength was calculated as an average of three parallel

cubes. Tables 29, 30 and figure 56 present the mix design average, standard deviation,

maximum value and minimum value for 1d, 28 d and 91d respectively for the six mix

designs. The standard deviation results, 1d, 28d and 91d, for the three parallel cubes is

presented in table 31.

Table 29. Compressive strength statistics for each mix design, 1d

1d [MPa] N30 N35 WR30 WR35 AE30 AE35 All

Average 15.0 14.4 20.9 20.0 17.7 15.7 17.1

Stdev 1.2 0.9 1.5 1.4 1.9 1.3 2.9

Max 18.0 16.3 24.3 23.5 20.7 19.3 24.3

Min 12.2 12.0 18.3 17.1 13.6 12.6 12.0

Table 30. Compressive strength statistics for each mix design, 28d

28d [MPa] N30 N35 WR30 WR35 AE30 AE35 All

Average 42.6 42.3 52.0 51.3 43.0 39.5 44.9

Stdev 3.0 2.0 4.5 2.9 2.7 2.5 5.7

Max 46.6 45.4 57.6 54.5 48.1 46.2 57.6

Min 34.6 36.8 40.4 42.5 37.6 34.5 34.5

111

Table 31. Standard deviation statistics for the three parallel cubes

C stdev [MPa] 1d 28d 91d

Average stdev 0.3 0.6 0.7

Stdev 0.1 0.3 0.3

Max 0.9 1.4 1.5

Min 0.0 0.1 0.1

Figure 56. Compressive strength statistics for each mix design, 91 d

The basis for the mix designs was two fixed amounts of cement, 300 kg/m3 and 350

kg/m3, and the same starting workability with one fines/semi-coarse aggregate

combination. This led to different w/c ratios and thus variable compressive strength

levels with different mix designs. However, within a mix design group the compressive

strength should be the same, because the w/c ratio was constant for the two amounts of

cement. At all ages, the WR mix designs have the highest compressive strength (w/c =

0.58) and the N and AE mix designs have approximately 20% lower values at each age

(w/c = 0.65 and 0.58 respectively). The AE30 mix design has higher compressive

strength than the AE35 mix design, because the air % attained is lower, and the strength

Compressive Strength, 91d

6.32.72.93.05.12.12.8=stdev

35

40

45

50

55

60

65

Co

mp

. str

eng

th [

MP

a]

avg [MPa] 47.8 47.2 58.2 57.7 47.8 44.1 50.2

m ax 51.7 51.7 64.0 60.8 52.4 50.3 64.0

m in 37.7 43.3 45.2 46.6 39.8 37.6 37.6


112

is thus reduced less. The average standard deviation for the three parallel compressive

strength cubes is half of the repeatability error between the repetition castings (table 32

and 31).

Table 32. Compressive strength repeatability error and group average


N WR AE All

Repeatability error [MPa] 2.2

(1.6-3.3)

2.5

(1.8-3.9)

1.6

(1.0-3.1)

2.1

(1.8-2.7)

Group Average [MPa] 4.3

(3.4-4.8)

9.3

(4.6-12.4)

4.9

(4.1-6.3)

4.8

(4.6-5.6)

For the N mix designs the repeatability error is approximately 50% of the group average,

and for the WR and AE mix designs the group average exceeds the repeatability error by

over 300%.

The air % has a strong influence on the achievable compressive strength. Figure 57a

shows the correlation for the WR mix designs (R = -0.73 for the WR30 and R = -0.53 for

the WR35), and figure 57b presents the correlation for the AE mix designs (R = -0.73).

The corresponding values for the N30 and N35 mix designs are R = -0.56 and R = -0.39

respectively.

Figure 57. Correlation between air % and compressive strength

Air % (AE) vs. Compressive Strenght 91d

R = -0.73

30

35

40

45

50

55

0 2 4 6 8


Co

pm

ress

ive

Str

eng

ht [

MP

a]

Air % (WR) vs. Compressive Strength 91d

R = -0.73

R = -0.53

45

50

55

60

65

0 1 2 3


Co

mp

ress

ive

Str

eng

th [

MP

a]

WR35

WR30

113

Other characteristics that have a strong influence and which can be seen from the linear

correlation calculations are the SC- Los Angeles value and SC- quantity of the particles.

Figure 58a presents the correlation between the SC- LA value (mod.) and compressive

strength at 91d for the WR mix designs. Figure 58b shows the correlation between SC-

particle quantity 3.15/4.0 mm and compressive strength for the N mix designs. The

correlation between the SC- quantity of the particles and the SC- LA value is R = 0.66.

Figure 58a. Correlation between SC- Los Angeles (mod.) value and

compressive strength at 91d, WR mix designs

Figure 58b. Correlation between SC- quantity 3.15/4.0 mm and

compressive strength at 91d, N mix designs

As can be seen, the effect of the SC- LA value and the SC-quantity can be is as high as

15…25 % of the absolute value of the compressive strength. The correlations with the

other mix designs are also significant, except in the case of AE30.

Los Angeles (mod.) -value (WR) vs. Compressive Strength 91d

R = -0.90

R = -0.85

40

50

60

70

20 25 30 35 40

Los Angeles (mod.) [%]

Co

mp

. Str

eng

th [

MP

a]

WR30

WR35

Quantity 3.15/4.0 mm (N) vs.Compressive Strength 91d

R = -0.82

R = -0.66

35

40

45

50

55

3.E+06 3.E+06 4.E+06 4.E+06 5.E+06

Quantity 3.15/4.0 mm

Com

p. S

tren

gth

[MP

a]

N30

N35

114

6.5 Drying shrinkage and weight change

6.5.1 Results

The shrinkage and weight measurements were conducted 10 to 13 times during the 231d

period during which the prisms were observed. For each casting, the results are

calculated as an average of two parallel prisms. The standard deviation statistics for the

231d results for two parallel prisms is presented in table 33. The repeatability errors for

the drying shrinkage and weight change are 0.060 ± 0.012 o/oo and 0.088 ± 0.020 %

respectively.

Table 33. Standard deviation statistics for the drying shrinkage and

weight change, two parallel prisms, 231d

[ o/oo ] Drying shrinkage Weight change [ % ]

Average stdev 0.011 0.054 Average stdev

Stdev 0.009 0.045 Stdev

Max 0.037 0.192 Max

Min 0.000 0.001 Min

Figures 59 and 60 show the average, standard deviation, maximum and minimum values

for the 231d drying shrinkage and weight change for three mix designs. The number of

castings per mix design was 15, 16 and 16 castings for N35, WR30 and AE35

respectively.

Figures 61 and 62 show the drying shrinkage and weight change development for

particular fines/semi-coarse combinations (7 castings) to verify the variations between

and within the mix designs.

115

Figures 59 and 60. Drying shrinkage and weight change statistics, 231d

Figures 61 and 62. Drying shrinkage and weight change due to the mix design variation

As can be seen from the results, the variation in drying shrinkage within a mix design is

greater than the variation between them. The maximum difference between the mix

designs is 20 % (WR30 – AE35), while the difference between max-min values within a

mix design is 43%, 39% and 28% for the N35, WR30 and AE35 respectively. This

indicates that the effect of the fine aggregate characteristics exceeds the effect of the mix

design.

Drying Shrinkage, 231d

0.0740.0540.0550.069 =stdev

0.5

0.6

0.7

0.8

0.9

Sh

rin

kag

e [o

/oo

]

avg [o/oo] 0.641 0.564 0.670 0.625

max 0.797 0.702 0.749 0.797

min 0.555 0.505 0.583 0.505

N35 WR30 AE35 All

Weight Change, 231d

0.4800.1320.1280.138=stdev

-3.0

-2.5

-2.0

-1.5

-1.0

Wei

gh

t ch

ang

e [%

]

avg [%] -2.566 -1.438 -2.009 -1.992

max -2.362 -1.181 -1.826 -1.181

min -2.884 -1.630 -2.242 -2.884

N35 WR30 AE35 All

Drying Shrinkage AVG (B1/B1, A8/A8, A16/A16, B11/B11, B2/B2, B13/B13, B8/B8)

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 50 100 150 200 250

Age [d]

Sh

rin

kag

e [o

/oo

]

N35

AE35

WR30

Weight ChangeAVG (B1/B1, A8/A8, A16/A16, B11/B11, B2/B2, B13/B13, B8/B8)

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

0 50 100 150 200 250

Age [d]

Wei

gh

t ch

ang

e [%

]

N35

AE35

WR30

116

The weight change is more strongly affected by the mix design than by the aggregate

characteristics. The variation between the mix designs is nearly 80 % (N35 – WR30),

while the variations within the mix designs are only 22%, 38% and 23% for the N35,

WR30 and AE35 respectively. If we calculate the weight change relative to the total

water amount, and compare the results to the weight change of the N35 mix design, we

observe that the weight changes of the WR30 and AE35 mix designs are 54% and 75%

respectively.

If the results are analysed from the point of view of aggregate products, we obtain

figures, which reveal the dominating aggregate characteristics. Figures 63 and 64 present

the drying shrinkage and weight change curves for the same 7 castings in such a way that

the curve represents the average of the three mix designs (N35, WR30, AE35) for each

aggregate combination.

Figure 63. Drying shrinkage scatter due to aggregate characteristics

Drying Shrinkage, AVG (N35,AE35, WR30)

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 50 100 150 200 250

Age [d]

Shrinka

ge

[o/o

o]

B1/B1

A8/A8

A16/A16

B11/B11

B2/B2

B13/B13

B8/B8

117

Figure 64. Weight change scatter due to aggregate characteristics

The largest shrinkage occurs in the case of the aggregate combinations B8/B8, B11/B11

and B1/B1, which all have a high SCF particle porosity and fines surface area (figure 30

and 32 chapter 5). A8/A8 and A16/A16 are both castings with only rock aggregate

products and do not have excessively high SCF particle porosity characteristics. A8

represents a poor particle shape and thus has a high quantity of particles, whereas A16

has a fair particle shape, which results in fewer particles as compared to the A8. Hence,

the shape quality of particles does not seem to affect the drying shrinkage if the loss of

workability is accepted and thus, the paste amount is kept constant.

The difference between the highest and lowest weight changes is not especially large, nor

can any aggregate quality characteristics particularly be related to the weight changes.

Figure 65 shows how much the shrinkage properties can be affected merely by a change

in the fines (<0.125 mm).

Weight Change, AVG (N35,AE35, WR30)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

0 50 100 150 200 250

Age [d]

Wei

gh

t ch

ang

e [%

]

B1/B1

A8/A8

A16/A16

B11/B11

B2/B2

B13/B13

B8/B8

118

Figure 65. Effect of the fines, < 0.125 mm on drying shrinkage

Even the B1 and B3 represent very different fines quality, the amount of fines in the

combined grading curve is only 3…4%, yet the effect on the shrinkage is approximately

5% (231d). The repeatability error is greater than the observed fine fraction effect, but as

the difference is calculated as an average of three mix designs, the phenomenon is

statistically valid.

6.5.2 Discussion

The weathering properties of the gravel aggregate increase the shrinkage. In the case of

weathered aggregates, the drying shrinkage is partly self-induced, as the water absorption

reduces the workability, extra water is needed for replacement; together with the time-

dependent water evaporation from the aggregate pores, this in turn further increase the

shrinkage. In the case of the present test programme, the effect of the aggregate

D rying Shrinkage - Effect of the fines AVG (N35, AE35, WR 30)

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 50 100 150 200 250

Age [d]

Sh

rin

kag

e o

/oo

B3/B1

B1/B1

119

characteristics on the drying shrinkage was more than twice as large as the effect of

changes in the mix design.

For drying shrinkage purposes, the best information on the potential shrinkage is

obtained if the water/cement ratio of the mix design is expressed by means of the total

water content instead of the effective amount of water. As the rock aggregate products

are not normally weathered, the shrinkage potential is thus lower than in the case of

normal gravel aggregates, but the possible extra water and/or paste needed for good

workability increases the tendency. However, the results of this study make it possible to

assume that even though corrective changes in the mix design were made, the shrinkage

increase is less that the effect of moderately weathered gravel aggregate. Consequently, if

a non-weathered gravel aggregate product is available, both the drying shrinkage and the

w/c ratio for good workability are low.

By means of aggregate production technology, it is possible to reduce the effect that the

fine aggregate has on the drying shrinkage. If the gravel fine aggregate is weathered, it is

possible to reduce the effect by using, for example, the following aggregate production

techniques:

• Crushing the gravel (totally or partly), because in this case fresh, unweathered

surfaces open

• Sieving off (dry or wet sieving) the finest fractions, because they have the largest

surface area and thus particle porosity and replacing them with unweathered filler

aggregate

It should, however, be remembered, that the fines are derived from the semi-coarse

particles and that the same characteristics are thus valid for all size fractions. Therefore,

if it is not possible to exclude a phenomenon, the effect and its magnitude should be

known and altered within the possible limits.

120

7 MODELS FOR THE FINE AGGREGATE – CONCRETE INTERACTION

7.1 Model for the flow value

The model for the flow value consists of 12 parameters: 3 mix design characteristics and

5 semi-coarse and 4 fines characteristics. Figure 66 shows the ARD listing for the

parameters.

Figure 66. ARD listing of the parameters for the modelled flow value

Asterisk =mean value; diamond =median value;

box = 25-75 % quantiles; line = 10-90 % quantiles

The median of the 90% quantile and its 10% and 90% quantiles for the model error are


Cu

Mica% fines

Pore area > 900 Å

Elgn 3.15/4.0 mm

Angularity

AE

Pore area 300-900 Å

Flkn 3.15/4.0 mm

WR

SEM

ZETA pot.

BET

-4 -3 -2 -1 0

121

Table 34. Flow group averages and repeatability and model errors

median of 90% quantile (10%-90% quantiles for 90% quantile)

N WR AE All

Repeatability error[mm] 21

(16-31)

9

(6-13)

10

(6-19)

15

(13-19)

Group Average [mm] 70

(62-89)

128

(122-139)

61

(48-72)

104

(97-119)

Model error [mm] 34

(32-41)

39

(33-47)

30

(26-34)

34

(32-38)

For the N and AE mix designs the model is 50% more accurate than the group average,

and for the WR mix designs the value is as high as 70%. The repeatability errors

represent 23% to 62% of the different mix design model errors and 44% of the model

error for all the mix designs. One workability class is 60 mm (EN206) and as the model

error is 34 mm (32-38), the model error constitutes approximately half a workability

class. Consequently, the repeatability error is a quarter of a workability class.

Figure 67 shows correlation plots for measured vs. modelled flow value for the AE mix

designs and the N&WR mix designs. Table 35 presents the correlations for each mix

design, for mix design groups and for all mix designs.

Figure 67. Correlation plots for measured vs. modelled flow value

AE mix designs and N & WR mix designs

Measured vs. modelled FLOW value, AE mix designs

R = 0.99

250

350

450

550

250 300 350 400 450 500 550

Measured flow value [mm]

Mod

elle

d fl

ow v

alue

[m

m]

Measured vs. modelled FLOW value, N & WR mix designs

R = 0.99

250

350

450

550

650

750

250 350 450 550 650 750

Measured flow value [mm]

Mod

elle

d fl

ow v

alue

[m

m]

122

Table 35. Correlations between measured and modelled flow value for

each mix design, mix design groups and all mix designs

7.1.1 Sensitivity analysis – flow value

7.1.1.1 Reliability of the sensitivity analysis – flow value

The reliability figures for the flow value sensitivity analyses are presented in appendix 4.

As can be seen from the figures, the deviations between the measured and the modelled

values in general constitute less than half a workability class. Greater differences are

mainly concentrated among the N30 castings.

7.1.1.2 Flow value – SC- flakiness 3.15/4.0 mm, SC- angularity and

SC- elongation 3.15/4.0 mm

The flakiness 3.15/4.0 mm has a strong effect on workability in all mix designs (figure

68). The effect is also approximately the same for all mix designs.

The angularity affects the workability of the WR mix designs most strongly. To some

extent, it also influences the N30 and AE30 mix designs, but the effect seems to be

negligible on the higher paste amount mix designs containing a higher amount of paste,

i.e. N35 and AE35 (figure 69).

The effect of the elongation 3.15/4.0 mm is not linear. In the case of the lower elongation

values, there is a negative effect on workability, but after certain limit, the increase in

elongation enhances workability (figure 70). However, the enhancing effect is dependent

on the amount of paste, so that this effect is stronger for mix designs with a higher paste

amount, i.e. N35, WR35 and AE35.

AE30 AE35 N30 N35 WR30 WR35 WR N N&WR AE ALL0.97 0.94 0.97 0.97 0.99 0.99 1.00 0.98 0.99 0.99 0.99

123

Figure 68. Sensitivity analysis figure; SC- flakiness 3.15/4.0–flow value

Figure 69. Sensitivity analysis–figure; SC- angularity–flow value


0

100

200

300

400

500

600

700

800

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Flo

w v

alu

e [m

m]

N30

WR30AE30

N35WR35

AE35


0

100

200

300

400

500

600

700

800

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

SC-Angularity

Flo

w v

alu

e [m

m]

N30W R30AE30N35W R35AE35

124

Figure 70. Sensitivity analysis figure; SC-elongation 3.15/4.0 mm–flow value

7.1.1.3 Flow value – SC- pore area 300-900 Å and SC- pore area > 900 Å

Both the SC- pore area values have significant effect on the flow value, (figures 71 and

72).

Figure 71. Sensitivity analysis figure; SC- pore area 300-900Å–flow value


0

100

200

300

400

500

600

700

800

1.34 1.36 1.38 1.4 1.42 1.44 1.46 1.48

SC-Elgn 3.15/4.0 mm

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

SC-pore area 300-900Å [m2/g]

Flo

w v

alu

e [m

m]


125

Figure 72. Sensitivity analysis figure; SC- pore area > 900Å–flow value

7.1.1.4 Flow value – >F- Mica %, F- Cu, F- BET value and F- zeta potential

The effect of the percentage of F- mica % fines alone seems to be negligible (figure 73),

and thus can be assumed that it acts in combination with other fines inputs. The situation

is the same in the case of F- zeta potential (figure 74).

The sensitivity analysis for F- Cu, figure 75, shows a slight general negative effect on

workability accompanying an increased F- Cu value. The effect is approximately equal

for all mix designs.

An increase in the F- BET value also has a negative effect on workability. However,

different behaviour is seen with different mix designs; the effect on mix designs

containing superplasticizer is the strongest (figure 76).


0

100

200

300

400

500

600

700

800

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

SC-pore area>900Å [m2/g]

Flo

w v

alu

e [m

m]


126

Figure 73. Sensitivity analysis figure; %F- mica fines–flow value

Figure 75. Sensitivity analysis figure; F- Cu–flow value


0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16

F - mica %

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16 18

F - Cu

Flo

w v

alu

e [m

m]

N30WR30

AE30

N35WR35

AE35

127

Figure 76. Sensitivity analysis figure; F- BET value–flow value

Figure 74. Sensitivity analysis figure; F- Zeta potential–flow value


0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Flo

w v

alu

e [m

m]

N30WR30

AE30

N35WR35

AE35


0

100

200

300

400

500

600

700

800

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

F - Zeta pot. [mV]

Flo

w v

alu

e [m

m]

N30WR30

AE30

N35WR35

AE35

128

7.2 Model for air %, fresh concrete

The model for the air % in fresh concrete consists of 9 parameters: 3 mix design

characteristics and 4 are semi-coarse and 2 fines characteristics. Figure 77 shows the

ARD listing for the parameters.

Figure 77. ARD listing of the parameters for the modelled air %

Asterisk = mean value; diamond = median value;




Table 36 Air % group averages and repeatability and model errors

median of 90% quantile (10%-90% quantiles for 90% quantile)N WR AE All

Repeatability error[%] 0.3(0.2–0.4)

0.1(0.1-0.2)

0.4(0.3-0.8)

0.3(0.2-0.3)

Group Average [%] 0.4(0.4-0.5)

0.8(0.6-0.8)

1.5(1.1-2.2)

0.9(0.8-1.0)

Model error [%] 0.2(0.2-0.3)

0.4(0.3-0.5)

0.9(0.8-1.1)

0.6(0.5-0.7)

Flk 3.15/4.0mm

Pore area 300-900Å

Angularity

WR

SEM

Pore area 60-300Å

AE

Cu

BET

-4 -3 -2 -1 0 1 2 3

129

When one compares the model errors to the group averages, the explication is enhanced

approximately 50 %. For all the mix designs the model error is twice the repeatability

error and 65 % of the group average model.

Figure 78 presents correlation plots for measured vs. modelled air % for the AE mix

designs and the N&WR mix designs. Table 37 below further presents the correlations for

each mix design, for mix design groups and for all mix designs.

Figure 78. Correlation plots for measured air % vs. modelled air %

Table 37. Correlations between measured air % and modelled air % for each mix

design, mix design group and all mix designs

7.2.1 Sensitivity analysis – air %

7.2.1.1 Reliability of the sensitivity analysis – air %

The reliability figures for the air % sensitivity analyses are presented in appendix 4. For

the N and WR mix designs the deviations between the modelled and measured values is

clearly less than 0.5%-unit. Generally this is also the case for the AE mix designs, though

there are several castings where the deviation is as high as 1.5%-unit.

Measured vs. modelled AIR %, N & WR mix designs

R = 0.98

0

1

2

3

0 1 2 3

Measured air %

Mo

del

led

air

%

Measured vs. modelled AIR %, AE mix designs

R = 0.95

2

4

6

8

2 3 4 5 6 7 8

Measured air %

Mo

del

led

air

%


130

7.2.1.2 Air % - SC- pore area 60-300Å and SC- pore area 300-900Å

The SC- pore area 60-300Å and 300-900Å have a similar effect on the mix designs, i.e.

the air % increases with the N and WR mix designs and decreases with the AE mix

designs (figure 79 and 80).

Figure 79. Sensitivity analysis figure; SC- pore area 60-300 Å–air %

At low amount of pore area the shape characteristics act together, causing the air % to

increase in the case of the AE30 and decrease in that of the N and WR mix designs.

Additionally noteworthy for both SC- pore area inputs is the fact that the decrease in the

air % is very dramatic in the range of medium-amount of the pore area, but seems to

recover at the higher-amount of pore area. This phenomenon is most likely due to

different aggregate products and thus difference in the pore structure. The measurements

were though conducted similarly for all aggregate products.


0

1

2

3

4

5

6

7

8

-0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16


Air

%

N30WR30

AE30

N35WR35

AE35

131

Figure 80. Sensitivity analysis figure; SC- pore area 300-900 Å–air %

7.2.1.3 Air % - SC- flakiness 3.15/4.0 mm and SC-angularity

When the SC- flakiness 3.15/4.0 mm increases, the effect of it on the two AE mix

designs is the opposite: the air % of the AE30 mix design decreases and the air % of the

AE35 mix design increases, to an even greater extent. The effect on the N and WR mix

designs is negligible (figure 81).

SC- angularity has a decreasing effect on the air % of the AE mix designs. The effect is

stronger in the case of the AE35. For the N and WR mix designs, SC- angularity has a

slight effect on the entrapped air % (figure 82).


0

1

2

3

4

5

6

7

8

-0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14


Air

%

N30WR30

AE30

N35WR35

AE35

132

Figure 81. Sensitivity analysis figure; SC- flakiness 3.15/4.0 mm–air %

Figure 82. Sensitivity analysis figure; SC- angularity–air %


0

1

2

3

4

5

6

7

8

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Air

%

N30WR30

AE30

N35WR35

AE35


0

1

2

3

4

5

6

7

8

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

SC-Angularity

Air

%

N30WR30

AE30

N35WR35

AE35

133

7.2.1.4 Air % - F- Cu and F- BET value

The effects of the F- Cu and F- BET value are not very strong, and for the N and WR mix

designs the influence is almost negligible (figures 83 and 84). When the F- Cu increases

the air % decreases and the influence is slightly stronger on the AE30 mix design. An

increase in the F- BET value also decreases the air % and seems to influence the AE35

mix design somewhat more strongly.

Figure 83. Sensitivity analysis figure; F- Cu–air %

Figure 84. Sensitivity analysis figure; F- BET value–air %


0

1

2

3

4

5

6

7

8

2 4 6 8 10 12 14 16 18

F - Cu

Air

%

N30WR30

AE30

N35WR35

AE35


0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Air

%

N30

WR30

AE30N35

WR35AE35

134

7.3 Model for the bleeding

The model for the bleeding 60 min consists of 12 parameters, 3 mix design

characteristics and 5 semi-coarse and 4 fines characteristics. The figure 85 shows the

ARD listing for the parameters. The median of the 90% quantile and its 10% and 90%

quantiles for the model error are presented in table 38.

Figure 85. ARD listing of the parameters for the modelled bleeding



Table 38. Bleeding group averages and repeatability and model errors


Repeatability error [g/cm3] 1.1(0.8-3.2)

2.2(1.5-4.0)

0.3(0.2-0.6)

1.3(1.1-1.7)

Group Average [g/cm3] 2.5(2.3-3.2)

3.6(2.2-7.8)

0.9(0.6-1.2)

2.3(2.0-2.5)

Model error [g/cm3] 2.2(1.8-2.8)

2.8(1.1-6.3)

0.7(0.4-0.9)

1.8(1.3-2.2)

WR

SEM

Tot. pore area SC

AE

Elgn 0.8/1.0 mm

Flkn 1.6/2.0 mm

BET

Avg pore size SC

Elgn 1.6/2.0 mm

ZETA pot.

Density fines

Cu

-4 -3 -2 -1 0 1 2 3

135

If the expectation values of the group average and model are compared, it will be noticed

that no major improvement has occurred. The most important reason for this is the large

deviation in the measurement, i.e. the considerable repeatability error.

Figure 86 presents correlation plots measured vs. modelled bleeding for the WR and N

mix designs. Additionally, table 39 shows the correlations for each mix design, for mix

design groups and for all mix designs.

Figure 86. Correlation plots for measured vs. modelled bleeding

Table 39. Correlations between measured and modelled bleeding

7.3.1 Sensitivity analysis – bleeding

7.3.1.1 Reliability of the sensitivity analysis - bleeding

The model follows the bleeding of all mix designs except the WR35 well. In this case the

model underestimates the higher bleeding values, and some deviations between the

modelled and measured values amount as much as 4 g/cm3. For the N30, AE30, AE35

and WR30 mix designs, which all exhibited low bleeding, the deviation is generally less

Measured vs. modelled BLEEDING, WR mix designs

R = 0.97

0

4

8

12

16

20

0 5 10 15 20

Measured bleeding [g/cm3]

Mo

del

led

ble

edin

g [

g/c

m3 ]

Measured vs. modelled BLEEDING, N mix designs

R = 0.88

0

2

4

6

8

10

0 2 4 6 8 10

Measured bleeding [g/cm3]

Mo

del

led

ble

edin

g [

g/c

m3 ]


136

than 0.5 g/cm3. For the N35 the highest deviations are 2 g/cm3. The reliability figures for

the bleeding sensitivity analyses are presented in appendix 4.

7.3.1.2 Bleeding – SC- total pore area and SC- average pore area

The SC- total pore area has an inhibiting effect on bleeding, and the effect is strongest on

the WR35 mix design (figure 87). Here we can also see the effect of different aggregate

products, i.e. bleeding seems to increase when the SC- total pore area increases (compare

air % model).

Figure 88 shows an extreme case in which the aggregate products have two strong inputs

whose effect works in opposite directions (amount of pore area >< shape) in the case of

the WR35 mix designs. Depending on the value of SC- average pore size, the net effect

on bleeding can be either inhibiting or promoting. For other mix designs, an increase in

the SC- average pore size slightly increases bleeding.

Figure 87. Sensitivity analysis figure; SC- total pore area-bleeding


0

2

4

6

8

10

12

14

16

18

20

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

SC-tot. pore area [m2/g]

Ble

edin

g [

g/c

m3]

N30

WR30

AE30N35

WR35AE35

137

Figure 88. Sensitivity analysis figure; SC- average pore size–bleeding

7.3.1.3 Bleeding–SC- Elongation 0.8/1.0mm, SC- flakiness 1.6/2.0mm and

SC-elongation 1.6/2.0mm

When the elongation 0.8/1.0 mm increases, it increases the bleeding of the WR35 mix

design. In the case of the N35 and WR30, an increase in elongation first increases the

bleeding, though at higher elongation values the bleeding decreases. For other mix

designs the effect is negligible (figure 89).

The flakiness 1.6/2.0 mm decreases bleeding in all the mix designs, but the effect is

strongest in the case of the WR35, N35 and WR30 mix designs (figure 90).

The effect of the elongation 1.6/2.0 mm alone is seemingly negligible in the case of mix

designs other than the WR35 (figure 91).


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12

SC- avg.pore size [µm]

Ble

edin

g [

g/c

m3]

N30WR30

AE30

N35WR35

AE35

138

Figure 89. Sensitivity analysis figure; SC- elongation 0.8/1.0 mm–bleeding

Figure 90. Sensitivity analysis figure; SC- flakiness 1.6/2.0 mm–bleeding


0

2

4

6

8

10

12

14

16

1.2 1.25 1.3 1.35 1.4 1.45 1.5

SC-Flkn 1.6/2.0 mm

Ble

edin

g [

g/c

m3]

N30

WR30AE30

N35WR35

AE35


0

2

4

6

8

10

12

14

16

18

20

1.4 1.45 1.5 1.55 1.6 1.65 1.7

SC-Elgn 0.8/1.0 mm

Ble

edin

g [

g/c

m3]

N30WR30

AE30

N35WR35

AE35

139

Figure 91. Sensitivity analysis figure; SC- elongation 1.6/2.0 mm–bleeding

7.3.1.4 Bleeding – F- BET value, F- zeta potential, F- density and F- Cu

The F- BET value strongly decreases the bleeding of the WR35 mix designs. This

influence is also fairly important in the case of the N35 mix. A slight inhibiting trend can

also be seen in the case of the WR30 mix design, but for the others, the influence is

negligible (figure 92).

When the absolute value of the F- zeta potential decreases, the bleeding of the WR35 and

N35 mix designs decreases. For other mix designs the influence is negligible (figure 93).

The F- density has an influence only on the WR35 mix designs. When the F- density

increases, the bleeding decreases (figure 94).

The increase of the F- Cu inhibits the bleeding of the N35 and WR35 mix designs. For

other mix designs the effect is mainly negligible (figure 95).


0

2

4

6

8

10

12

14

16

1.3 1.35 1.4 1.45 1.5 1.55 1.6

SC-Elgn 1.6/2.0 mm

Ble

edin

g [

g/c

m3]

N30

WR30AE30

N35WR35

AE35

140

Figure 92. Sensitivity analysis figure; F- BET value–bleeding

Figure 93. Sensitivity analysis figure; F- Zeta potential–bleeding


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Ble

edin

g [

g/c

m3]

N30

WR30AE30

N35WR35

AE35


0

2

4

6

8

10

12

14

16

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

F - Zeta pot. [mV]

Ble

edin

g [

g/c

m3]

N30WR30

AE30

N35WR35

AE35

141

Figure 94. Sensitivity analysis figure; density fines–bleeding

Figure 94. Sensitivity analysis figure; F- Cu bleeding


0

2

4

6

8

10

12

14

16

18

20

2.6 2.65 2.7 2.75 2.8 2.85 2.9 2.95 3 3.05

F- density [Mg/m3]

Ble

edin

g [

g/c

m3]

N30

WR30AE30

N35WR35

AE35


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18

F - Cu

Ble

edin

g [

g/c

m3]

N30WR30

AE30

N35WR35

AE35

142

7.4 Model for the compressive strength

The model for the compressive strength 91d consists of 7 parameters: 3 mix design

characteristics and 4 semi-coarse characteristics. Figure 96 shows the ARD listing for the

parameters.

Figure 96. ARD listing of the parameters for the modelled compressive strength





Table 40. Compressive strength group averages and repeatability and model errors


Repeatability error[MPa] 2.2(1.6-3.3)

2.5(1.8-3.9)

1.6(1.0-3.1)

2.1(1.8-2.7)

Group Average [MPa] 4.3(3.4-4.8)

9.3(4.6-12.4)

4.9(4.1-6.3)

4.8(4.6-5.6)

Model error [MPa] 1.9(1.7-2.6)

2.4(2.0-2.9)

3.1(2.8-3.4)

2.7(2.4-3.0)

WR

Flkn 3.15/4.0 mm

AE

Los Angeles

QNTY 1.6/2.0 mm

Pore area 10-300 Å

SEM

-4 -3 -2 -1 0 1

143

The best improvement with the model is achieved for the WR group, where the accuracy

improved with 75% compared to the group average. For the N and WR mix designs the

model and repeatability errors are virtually the same, but for the AE mix designs the

model error is still twice the repeatability error. On the other hand, the 90% quantile for

the expectation value of the repeatability is large and thus also affects the model error.

Figure 97 shows the correlation plots for measured vs. modelled compressive strength at

91d for the AE mix designs and the N & WR mix designs. In addition, table 41 below

presents the correlations for each mix design, for mix design groups and for all mix

designs.

Figure 97. Correlation plots for measured vs. modelled compressive strength 91d

Table 41. Correlations between measured and modelled compressive strength for each

mix design, for mix design groups and for all mix designs


Measured vs. modelledCOMPRESSIVE STRENGTH, AE mix designs

R= 0.84

35

40

45

50

55

35 40 45 50 55

Measured Comp. strength [MPa]

Mo

del

led

Co

mp

. str

eng

th [M

Pa]

Measured vs. modelledCOMPRESSIVE STRENGTH, N&WR mix designs

R= 0.98

35

40

45

50

55

60

65

35 40 45 50 55 60 65

Measured Comp. strength [MPa]

Mo

del

led

Co

mp

. str

eng

th [M

Pa]

144

7.4.1 Sensitivity analysis – compressive strength

7.4.1.1 Reliability of the sensitivity analysis – compressive strength

The reliability figures for the compressive strength 91d sensitivity analyses are presented

in appendix 4. In general the deviation between the modelled and measured value is less

than 3 MPa, but there are some castings where the deviation is as high as 7 MPa. The

greater deviations normally involve the AE35, WR30 and AE30 mix designs.

7.4.1.2 Compressive strength – SC- flakiness 3.15/4.0 mm and SC- quantity 1.6/2.0 mm

Both the SC- flakiness 3.15/4.0 mm and SC- quantity 1.6/2.0 mm have a powerful

decreasing effect on compressive strength (figures 98 and 99). The effect is

approximately the same in all mix designs.

Figure 98. Sensitivity analysis figure; SC- flakiness 3.15/4.0 mm–

compressive strength


35

40

45

50

55

60

65

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Co

mp

. Ste

ng

th [

MP

a]

N30WR30

AE30

N35WR35

AE35

145

Figure 99. Sensitivity analysis figure; SC- quantity 1.6/2.0 mm–compressive strength

7.4.1.3 Compressive strength – SC- Los Angeles value

The SC- Los Angles value also strongly affects the compressive strength. When the SC-

LA value increases, i.e. resistance to fragmentation decreases, the compressive strength

diminishes (figure 100). The tendency is similar for all mix designs, however, the effect

is strongest on the WR mix designs.

7.4.1.4 Compressive strength – SC- pore area 60-300 Å

The SC- pore area 60-300 Å has a minor effect on the compressive strength (figure 101).

Some increasing effect can be observed when the SC- pore area increases in the lower

amount of pore area.


35

40

45

50

55

60

65

14000000 15000000 16000000 17000000 18000000 19000000 20000000

SC-Qnty 1.6/2.0 mm

Co

mp

. Ste

ng

th [

MP

a]

N30WR30

AE30

N35WR35

AE35

146

Figure 100. Sensitivity analysis figure; SC- Los Angeles value–compressive strength

Figure 101. Sensitivity analysis figure; SC- pore area 60 - 300 Å–

compressive strength


35

40

45

50

55

60

65

20 22 24 26 28 30 32 34 36 38 40

SC-LA value (mod.) [%]

Co

mp

. Ste

ng

th [

MP

a]

N30WR30

AE30

N35WR35

AE35


35

40

45

50

55

60

65

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16


Co

mp

. Ste

ng

th [

MP

a]

N30

WR30

AE30N35

WR35AE35

147

7.5 Discussion of the models

For each concrete property (flow value, air %, bleeding and compressive strength) was

constructed one model. The concrete property model covers the effect of the relevant

aggregate characteristics in the six mix designs (see table 42, column “All”), i.e. the low

and high amount of paste without any admixture or with an air-entraining agent or with

superplasticizer.

Table 42 presents a summary of the concrete test results given in chapter 6. The six mix

design columns show how large the difference was between the highest and lowest

values within same mix design. The column “All” was calculated (max-min) from the

values for all the mix designs and hence it includes the additional difference from the

mix design changes i.e. the amount of paste and admixture.

Table 42. Summary of the concrete test results.

Difference between the maximum-minimum values and

the repeatability error

Difference between the maximum-minimum values

(Repeatability error)


Flow value [mm] 205

(21)

150

(21)

295

(9)

205

(9)

185

(10)

160

(10)

480

(15)

Air % [%] 1.1

(0.3)

1.1

(0.3)

1.8

(0.1)

1.3

(0.1)

4.5

(0.4)

4.5

(0.4)

7.3

(0.3)

Bleeding

60 min

[g/cm3] 6.0

(1.1)

8.7

(1.1)

2.4

(2.2)

18.2

(2.2)

2.2

(0.3)

2.8

(0.3)

18.4

(1.3)

Compressive

strength 91d

[MPa] 14.0

(2.2)

8.4

(2.2)

18.8

(2.5)

14.2

(2.5)

12.6

(1.6)

12.7

(1.6)

26.4

(2.1)

The ARD listing gives the order of the influence that each parameter has in the model.

The sensitivity analysis figures enable to visualisation of how the model reacts to

changes in one input variable in each mix design. As seen from the sensitivity analysis,

148

the influence of the aggregate characteristics varied considerably between the different

mix designs. The differences in behaviour are related mainly to the amount of paste and

the use of superplasticizer i.e. the WR mix designs.

Flow value model

For the flow value, the amount of paste (SEM) and the superplasticizer (WR) were the

two superior parameters in the model. The most important aggregate inputs were the

shape and porosity characteristics of the semi-coarse fractions. The fines-related input

characteristics, including mineralogy, fineness, effect of the superplasticizer (zeta

potential) and surface area, ranked only third in the flow value model.

The correlations between the measured and modelled flow values were excellent for each

mix design. The lowest correlation was observed for the AE35 (0.94) and the highest for

the WR mix designs (0.99). The correlation for all 215 castings was 0.99. The

repeatability error represents 44% of the model error.

Air % model

As air entrainment is induced into the concrete by the air entrainment admixture, it is

evident that the AE was the most important input. The porosity characteristic of the semi-

coarse fractions was clearly the second in the order and the most important aggregate

input. The amount of paste (SEM) and superplasticizer (WR) were the third and fourth

most important characteristics, followed by the shape characteristic of the semi-coarse

fraction and two fines inputs describing the fineness and the surface area.

The correlations between the measured and modelled air % were good for each mix

design. The lowest correlation was for the N30 (0.90) and the highest for the N35 mix

design (0.95). The correlation for all 215 castings was 0.99. The repeatability error

represents 50% of the model error.

149

Bleeding model

The mix design input WR was by far the most important input in the bleeding model.

The second important was the amount of paste and the AE ranked fourth. The porosity

characteristic of the semi-coarse fraction ranked the highest of the aggregate inputs,

followed by shape characteristics. In addition, the surface area characteristic of the fines

was fairly important in the model. The effect of the superplasticizer on the fines (zeta

potential) as well as the density of the fines were also included in the bleeding model.

The last on the list was the fineness of the fines.

The correlations between the measured and modelled bleeding were good for the WR35

mix designs; although the model underestimates the amount of bleeding. The correlations

for the WR30, N and AE mix designs were fair, between 0.81 and 0.88. The lowest

correlation was for the N30 (0.81), the highest for the WR35 mix design (0.96). The

correlation for all 213 castings was 0.96. The repeatability error represents 72% of the

model error.

Compressive strength model

The WR mix designs had the lowest w/c ratio, and as the superplasticizer additionally

increases the dispersion of the cement particles, it is understandable that what had the

greatest influence in the model. Air entrainment ranked third and the amount of paste

was second last on the list. The shape characteristic of the semi-coarse fraction was the

aggregate characteristic with the greatest influence and it was nearly as important as the

Los Angeles value. The pore area of the semi-coarse fraction also had some effect on the

compressive strength.. No fines characteristics were included in the model.

The correlation between the measured and modelled compressive strength was excellent

for the WR30 mix design and good for the WR35 and N30 mix designs. For the N35 and

the AE mix designs, the correlations were fair; between 0.75 and 0.88. The lowest

correlation was observed for the AE30 (0.75) and the highest for the WR30 mix design

(0.96). The correlation for all 215 castings was 0.97. The repeatability error represents

78% of the model error.

150

8. PREDICTING WITH THE MODELS

8.1 Principles of the predictions

8.1.1 Combined effect of two input characteristics

In these predictions, the combined effect on one output (concrete property) of two input

characteristics (aggregate characteristics) over their total variation range is shown by

means of 3D surface charts, figure 102.

Figure 102. A schematic 3D surface chart presenting how the variation in

two input values affect the value of one output, specific to one mix design

The charts are drawn for each mix design separately, as the mix design parameters (SEM,

N/AE/WR) are additional influencing characteristics for the output. In the 3D surface

chart calculations, the other characteristics of the model have fixed values.

The basic set of input characteristics was chosen according to the following principles,

which represent one possible future solution for a concrete aggregate combination

(appendix 6):

20

24

28

32

361.23 1.26 1.28 1.30 1.32 1.34 1.36 1.38 1.41 1.43

52

54

56

58

60

62

OU

TP

UT

INPUT 2

INPUT 1

C 91dWR35

60-62

58-60

56-58

54-56

52-54

151

• The fines fraction consists of

75% fines from unweathered filler aggregate

25% fines from crushed rock with a medium resistance to fragmentation

• The semi-coarse fractions consist of

100% crushed rock with fair shape, medium resistance to fragmentation and low

weathering degree

There would be ( )N2 different combinations (N = number of inputs in a model) to present

as 3D surface charts and therefore the example charts are drawn using representatives

which have the strongest influence in each model (see chapter 7) and within the

dependent characteristics, e.g. SC-shape, SC-weathering and F-fineness (see chapter

5.10). The surfaces in the 3D charts present the predicted expectation values calculated

by the models. The quantiles for the predicted expectation values vary according to the

amount and accuracy of the available training data (=215 castings) in the surroundings of

the two input-one output combination. The coefficient of variations for the expectation

values are reported in appendix 6 and have been calculated on the assumption that the

distributions are symmetrical.

8.1.2 Predictions with different solutions for concrete aggregate combination

In these predictions all the input characteristics are changed according to the aggregate

combination. The results are shown using a column chart including the 10%-90%

quantiles for the predicted expectation value for the output.

The prediction examples were performed to aggregate combinations with the following

rough generalising principles (appendix 5):

152

1. PAST GRAVEL

- represents gravel aggregate (coarse 0/4..8 mm) that has mainly run out or

cannot be extracted because of environmental restrictions

good quality, unweathered gravel

2. FUTURE GRAVEL

- represents gravel aggregate (coarse 0/4..8 mm) that is available to extract

compromised quality, i.e. (somewhat) weathered gravel

3. COMBINATION OF FILLER AGGREGATE AND CRUSHED ROCK

- represents a mixture of filler gravel aggregate and crushed rock aggregate

that can be extracted and produced

- the fines fraction accords the principle given in chapter 8.1.1

(75 % filler aggregate and 25% crushed rock)

- the weathering degree of the semi-coarse fractions (SCFs) is low

- the processing of the aggregate product or disregarded/inappropriate quality

requirements can cause performance variations in the concrete, and thus the

following quality combinations of the SCFs were additionally chosen for

predictions:

• good Shape and good Strength = gSHgST

• poor Shape and good Strength = pSHgST

• poor Shape and poor Strength = pSHpST

• good Shape and poor Strength = gSHpST

153

8.2 Predicting with the flow value model

8.2.1 SC- pore area 300-900 Å and SC- flakiness 3.15/4.0 mm vs. flow value

Figure 103 shows the combined effect of the SC- pore area 300-900Å and SC- flakiness

3.15/4.0 mm on the flow value for the six mix designs. The ranges of the SC- pore area

300-900 Å and the SC- flakiness are 0…0.13 m2/g and 1.23…1.43 respectively. The data

and corresponding coefficient of variation values are presented in appendix 6.

As can be seen from figure 103, there exists an apparent level difference between the low

and high paste mix designs. The effect of the increased amount of paste generally

exceeds the combined effect of SC- pore area and SC- flakiness, though there is some

overlapping. For all mix design groups the average flow value of the high paste mix

designs is greater than the maximum value for the low paste mix design (table 43).

Table 43. Statistics for the predicted flow value for each mix design; combined

effect of the SC- pore area 300-900Å and SC- flakiness 3.15/4.0 mm

N30 N35 AE30 AE35 WR30 WR35

Min [mm] 304 421 268 377 306 459

Max [mm] 470 570 427 503 535 700

Average [mm] 364 491 322 432 382 588

Stdev [mm] 50 43 45 39 64 67

The shape of the surfaces and the balance between the two characteristics on the surfaces

of the low paste mix designs are fairly identical. With a combination of low SC- pore

area and SC- flakiness, the flow value attains the highest values. The effect of the SC-

flakiness is, however, marginally greater than that of the SC- pore area.

With the high paste mix designs, the N35 and AE35 have similar types of surfaces, i.e.

when the SC- flakiness is low the effect of the SC- pore area is practically negligible. For

154

the WR35, however, the importance of the SC- pore area and SC- flakiness is

approximately equal to the flow value.

The superplasticizer enhances the quality differences between fine aggregate products

(table 43). In particular, the superplasticizer increases the workability of combinations

whose characteristics already produce best workability.

The coefficient of variation is highest in the area where both the SC- pore area and SC-

flakiness have high values, i.e. weathered and poor-shaped rock, which would normally

not be possible. Table 44 shows the coefficient of variation statistics for the predicted

flow value. More detailed information is given in appendix 6.

Table 44. Coefficient of variation statistics for the predicted flow value

for each mix design; combined effect of

the SC- pore area 300-900Å and SC- flakiness 3.15/4.0 mm

N30 N35 AE30 AE35 WR30 WR35

Min [%] 5 3 6 4 5 3

Max [%] 33 24 38 27 35 24

Average [%] 15 10 16 12 15 10

Stdev [%] 7 5 8 5 8 5

155

Figure 103. Combined effect of the SC- pore area 300-900Å and

SC- flakiness 3.15/4.0 mm on the flow value, all mix designs

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Flow N30

550-600

500-550

450-500

400-450

350-400

300-350

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Flow N35

550-600

500-550

450-500

400-450

350-400

300-350

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

250

300

350

400

450

500

550

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Flow AE30

500-550

450-500

400-450

350-400

300-350

250-300

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

250

300

350

400

450

500

550

Flo

w v

alu

e [m

m]

SC-pore area 300-900Å [m2/g]SC

-Flk

n 3/

4

Flow AE35

500-550

450-500

400-450

350-400

300-350

250-300

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

650

700

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Flow WR30

650-700

600-650

550-600

500-550

450-500

400-450

350-400

300-350

0.000.010.030.040.060.070.090.100.120.13

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

650

700

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Flow WR35

650-700

600-650

550-600

500-550

450-500

400-450

350-400

300-350

156

8.2.2 F- Cu and SC- flakiness 3.15/4.0 mm vs. flow value

Figure 104 shows the combined effect of the F- Cu and SC- flakiness 3.15/4.0 mm on the

flow value for the six mix designs. The ranges of F- Cu and SC- flakiness are 3.4…16.5

and 1.23…1.43 respectively. The data and corresponding coefficient of variation values

are presented in appendix 6.

The low and high paste surfaces are all fairly similar to each other, though the effect of

the SC- flakiness is slightly smaller in the case of mix designs with a high paste content.

Figure 104 and table 45 again show that in general, the amount of paste has a greater

effect on the flow value than the combined aggregate characteristics, though there is

some overlapping in the flow values of the same mix design group. The range of the flow

values is greater in the case of the low paste mix designs than in that of the high paste

mix designs, i.e. the additional paste and/or water reduces the combined effect of the F-

Cu and SC- flakiness.

Table 45. Statistics for the predicted flow value for each mix design;

combined effect of the F- Cu and SC- flakiness 3.15/4.0 mm

N30 N35 AE30 AE35 WR30 WR35

Min [mm] 332 465 302 414 331 586

Max [mm] 489 569 449 516 551 710

Average [mm] 394 513 353 457 428 663

Stdev [mm] 45 26 43 25 66 32

The flow value is highest when the F- Cu and SC- flakiness both have low values, and

correspondingly, it shows the lowest values when there is high F- Cu and SC- flakiness.

Again it can be noticed that the superplasticizer spreads the difference between the

extreme combinations.

The coefficient of variation statistics is presented in table 46. These values are

significantly smaller than the values for the SC- pore area and SC- flakiness

157

combination. As the fines and semi-coarse fractions were separated from each other, it

was possible to produce data over the whole combination range and thus, the quantiles

become smaller. More detailed information is given in appendix 6.

Table 46. Coefficient of variation statistics for the predicted flow value for each mix

design; combined effect of the F- Cu and SC- flakiness 3.15/4.0 mm

N30 N35 AE30 AE35 WR30 WR35

Min [%] 5 3 6 4 5 3

Max [%] 11 8 13 8 12 7

Average [%] 7 5 8 6 7 4

Stdev [%] 2 1 2 1 2 1

158

Figure 104. Combined effect of the Cu and flakiness 3.15/4.0 mm on the

flow value, all mix designs

356891112141516

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

Flow N30

550-600

500-550

450-500

400-450

350-400

300-350

356891112141516

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

Flow N35

550-600

500-550

450-500

400-450

350-400

300-350

356891112141516

1.23

1.28

1.32

1.36

1.41

250

300

350

400

450

500

550

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

Flow AE30

500-550

450-500

400-450

350-400

300-350

250-300

356891112141516

1.23

1.28

1.32

1.36

1.41

250

300

350

400

450

500

550

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

Flow AE35

500-550

450-500

400-450

350-400

300-350

250-300

356891112141516

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

650

700

750

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

FlowWR30

700-750

650-700

600-650

550-600

500-550

450-500

400-450

350-400

300-350

356891112141516

1.23

1.28

1.32

1.36

1.41

300

350

400

450

500

550

600

650

700

750

Flo

w v

alu

e [m

m]

F - Cu SC-F

lkn

3/4

FlowWR35

700-750

650-700

600-650

550-600

500-550

450-500

400-450

350-400

300-350

159

8.2.3 Effect of different aggregate combinations on the flow value

Figure 105 presents predicted flow values including 10% and 90% quantiles for six

different aggregate combinations. The combinations are described in chapter 8.1.2.

Figure 105. Predicted flow values for six different aggregate combinations

The past gravel (PG) achieves the best flow values in the case of all mix designs. The

difference between the PG and future gravel (FG) constitutes approximately one

workability class, i.e. 60 mm, for the N and AE mix designs,while for the WR castings

the difference increases to two workability classes.

The flow values for the good-shaped crushed rock product (gSH) are at the same level as

in the case of the FG mix designs, with the exception of the WR35 mix design; in this

case the gSH attains the flow value of the PG. For the poor-shaped crushed rock (pSH)

the workability loss is severe for the mix designs containing the low amount of paste, but

when the paste amount is high mix designs the pSH mix designs achieve the flow values

of the FG.

Flow [mm] - predicted values incl. 10%-90% quantiles

gSH - pSTpSH - pSTpSH - gSTgSH - gSTFUTUREeeGRAVEL

WR30/35

AE30/35

N30/35

PAST…..hj GRAVEL

200

300

400

500

600

700

800

160

8.3 Predicting with the air % model

8.3.1 SC- Pore area 60-300 Å and SC- flakiness 3.15/4.0 mm vs. air %

Figure 106 shows the combined effect of the SC- pore area 60-300Å and SC- flakiness

3.15/4.0 mm on the air % for the AE mix designs. The ranges of the Sc- pore area and the

SC- flakiness are 0…0.14 m2/g and 1.23…1.43 respectively. The data and corresponding

coefficient of variation values are presented in appendix 6.

In both the AE mix designs the air % is mainly influenced by the SC- pore area. For the

higher SC- pore area values, the air % is well below the target value of 5%, and when the

SC- pore area value reduces approximately to the level of 0.05 m2/g the air % rises

rapidly. In the case of the low paste mix design the SC- flakiness tends to decrease the air

% while for the high paste mix design the Sc- flakiness increases the air %.

The quantiles for the expectation values are very high in the case of the higher SC- pore

area values. This is due to a lack of training data, i.e. no combination of high SC- pore

area and high SC- flakiness, and the different behaviour of aggregate products in the case

of the SC- pore area. Table 47 shows the statistics for the predicted air %. More detailed

information is given in appendix 6.

Table 47. Statistics for the predicted air %, AE mix designs

combined effect of the SC- pore area 60-300Å and SC- flakiness 3.15/4.0

mm

Predicted air % [%] Coefficient of variation [%]

AE30 AE35 AE30 AE35

Min 1.8 2.3 13 11

Max 5.1 6.6 111 112

Average 2.6 3.4 69 67

Stdev 1.0 1.2 33 32

161

Figure 106. Combined effect of the SC- pore area 60-300Å and

SC- flakiness 3.15/4.0 mm on the air %, AE mix designs

8.3.2 SC- pore area 60-300 Å and F- Cu vs. air %

Figure 107 shows the combined effect of the SC- pore area 60-300Å and F- Cu on the air

% for the AE mix designs. The ranges of the SC- pore area and F- Cu are 0…0.14 m2/g

and 3.4…16.5 respectively. The data and corresponding coefficient of variation values

are presented in appendix 6.

The combined effect of the SC- pore area and F- Cu is very similar to the effect of the

SC- pore area and SC- flakiness. The air % is low for high SC- pore area values and rises

when the pore area of the semi-coarse fraction decreases. When the semi-coarse fraction

has low pore area values, the influence of the F- Cu can be detected. The air % becomes

higher if the Cu value of the fines is low.

The quantiles for the expectation values are very high with the higher SC- pore area

values. This is due to the different behaviour of aggregate products in the case of the SC-

pore area. Table 48 shows the statistics for the predicted air %. More detailed

information is given in the appendix 6.

0.00 0.02 0.03 0.05 0.06 0.08 0.10 0.11 0.13 0.141.23

1.28

1.32

1.36

1.41

1

2

3

4

5

6

7

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Air %AE30

6-7

5-6

4-5

3-4

2-3

1-2

0.00 0.02 0.03 0.05 0.06 0.08 0.10 0.11 0.13 0.141.23

1.28

1.32

1.36

1.41

1

2

3

4

5

6

7

Flo

w v

alu

e [m

m]


SC-F

lkn

3/4

Air %AE35

6-7

5-6

4-5

3-4

2-3

1-2

162

Table 48. Statistics for the predicted air %, AE mix designs

combined effect of the SC- pore area 60-300Å and F- Cu

Predicted air % [%] Coefficient of variation [%]

AE30 AE35 AE30 AE35

Min 1.7 2.3 15 12

Max 4.7 5.9 108 100

Average 2.6 3.4 68 64

Stdev 0.9 1.2 34 33

Figure 107. Combined effect of the SC- pore area 60-300Å and F- Cu on the air %,

AE mix designs

0.00 0.02 0.03 0.05 0.06 0.08 0.10 0.11 0.13 0.143

6

9

12

15

1

2

3

4

5

6

7

Flo

w v

alu

e [m

m]


F - C

u

Air %AE30

6-7

5-6

4-5

3-4

2-3

1-2

0.00 0.02 0.03 0.05 0.06 0.08 0.10 0.11 0.13 0.143

6

9

12

15

1

2

3

4

5

6

7

Flo

w v

alu

e [m

m]


F - C

u

Air %AE35

6-7

5-6

4-5

3-4

2-3

1-2

163

8.3.3 Effect of different aggregate combinations on the air %

Figure 108 presents predicted air % values including 10% and 90% quantiles for six


Figure 108. Predicted air % for six different aggregate combinations

For the N and WR mix designs the air % describes the amount of unintentionally

entrapped air i.e. air that is in the concrete due to unsuccessful compaction and low

workability. Generally can be noticed that the entrapped air is higher for the mix designs

containing the low amount of paste and for the poor-shaped aggregate products. The mix

designs containing the high amount of paste and the crushed products have entrained air

% values equal to those mix designs made with the past and future gravel products. By

contrast, the low paste AE30 containing crushed aggregate product attains significantly

lower air % values than those of the corresponding high paste mix design. The difference

is even greater if the shape is bad.

AIR% -predicted values incl. 10%-90% quantiles

gSH - pST..pSH - pST..pSH - gST..gSH - gST..

N30/35

AE30/35

WR30/35

PAST…..…. GRAVEL

FUTURE.....GRAVEL

0

1

2

3

4

5

6

7

164

8.4 Predicting with the bleeding model

8.4.1 SC- total pore area and F- BET value vs. bleeding

Figure 109 shows the combined effect of the SC- total pore area and F- BET on the

bleeding for the N35 and WR35 mix designs. The ranges of the SC- total pore area and

F- BET are 0.01…0.34 m2/g and 1.3…14.0 m2/g respectively. The data and

corresponding coefficient of variation values are presented in appendix 6.

For the N35 mix design the effect of the fines, i.e. the F- BET value, is stronger than the

effect of the semi-coarse, i.e. the SC- total pore area. The bleeding is highest when the F-

BET value and SC- total pore area both have low values, i.e. the combined pore area of

the fines and semi-coarse factions is the smallest.

For the WR35 mix design the effect of the F- BET and SC- total pore area is roughly

equal. The bleeding phenomenon of the mix designs N35 and WR35 differ from each

other. When the bleeding of the N35 mix design increases linearly as the combined pore

area decreases, the bleeding of the mix design WR35 is low, until a threshold value for

the combined pore area has been reached, after which the bleeding increases drastically.

The quantiles of the expectation values are very high over the whole 3D surface area.

Reasons for this include the different behaviour of aggregate products regarding the pore

area characteristics, inaccuracies in the measurements and small absolute values. It has

also already been noted that the model underestimates the high bleeding values, and thus

the quantiles become additionally larger in this area. Table 49 shows the statistics for the

predicted bleeding. More detailed information is given in appendix 6.

165

Table 49. Statistics for the bleeding, N35 and WR35 mix designs

combined effect of SC- total pore area and F- BET value

Predicted bleeding [g/cm3] Coefficient of variation [%]

N35 WR35 N35 WR35

Min 3.3 0.2 28 47

Max 7.5 13.3 194 147

Average 4.5 1.8 86 87

Stdev 1.1 2.3 43 27

Figure 109. Combined effect of the SC- total pore area and F- BET on the bleeding;

N35 and WR35 mix designs

8.4.2 SC- total pore area and SC- elongation 0.8/1.0 mm vs. bleeding

Figure 110 shows the combined effect of the SC- total pore area and SC- elongation

0.8/1.0 mm on the bleeding for the N35 and WR35 mix designs. The ranges of the SC-

total pore area and SC- elongation 0.8/1.0 mm are 0.01…0.34 m2/g and 1.45…1.64

respectively. The data and corresponding coefficient of variation values are presented in

appendix 6.

0.010.05

0.080.120.160.190.230.260.300.34

14

7

10

13

0

2

4

6

8

10

12

14

Ble

edin

g [g

/cm

3 ]


BET [m2/g

]

BleedingWR35

12-14

10-12

8-10

6-8

4-6

2-4

0-2

0.010.05

0.080.120.160.190.230.260.300.34

1

4

7

10

13

0

1

2

3

4

5

6

7

Ble

edin

g [g

/cm

3 ]

SC-tot. pore area [m2/g]BET [m

2/g]

BleedingN35

6-7

5-6

4-5

3-4

2-3

1-2

0-1

166

For the N35 mix design the effect of the SC- total pore area and SC- elongation is

roughly equal. The highest bleeding values occur when the SC- total pore area is low and

the SC- elongation has average range values.

For the WR35 mix design the SC- total pore area has a much greater impact than the SC-

elongation. In the high SC- total pore area the bleeding values are very minimal, and

after a threshold value for the SC- total pore area has been reached, the bleeding

increases rapidly. The highest bleeding value occurs when the SC- total pore area is low

and the SC- elongation is high.

The quantiles of the expectation values are very high over the whole 3D surface area.

Reasons for this include the different behaviour of aggregate products regarding the pore

area characteristics, inaccuracies in the measurements and small absolute values. It has

also already been noted that the model underestimates the high bleeding values, and thus

the quantiles become additionally larger in this area. Table 50 shows the statistics for the

predicted bleeding. More detailed information is given in appendix 6.

Table 50. Statistics for the bleeding, N35 and WR35 mix designs

combined effect of SC- total pore area and SC- elongation 0.8/1.0 mm

Predicted bleeding [g/cm3] Coefficient of variation [%]

N35 WR35 N35 WR35

Min 2.3 0.9 26 27

Max 6.8 11.2 220 148

Average 4.9 3.2 70 77

Stdev 1.2 2.6 39 28

167

Figure 110. Combined effect of the SC- total pore area and SC- elongation 0.8/1.0 mm

on the bleeding; N35 and WR35 mix designs

8.4.3 Effect of different aggregate combinations on the bleeding

Figure 111 presents predicted bleeding values includeing 10% and 90% quantiles for six


Figure 111. Predicted bleeding for six different aggregate combinations

0.01 0.05 0.08 0.12 0.16 0.19 0.23 0.260.30

0.34

1.45

1.50

1.54

1.58

1.62

0

1

2

3

4

5

6

7

Ble

edin

g [g

/cm

3 ]

SC-tot. pore area [m2/g] SC-Elg

n 0.8/1

Bleeding N35

6-7

5-6

4-5

3-4

2-3

1-2

0-1

0.01 0.05 0.08 0.12 0.16 0.19 0.23 0.260.30

0.34

1.45

1.50

1.54

1.58

1.62

0

1

2

3

4

5

6

7

Ble

edin

g [g

/cm

3 ]

SC-tot. pore area [m2/g] SC-Elgn 0.8/1

BleedingWR35

6-7

5-6

4-5

3-4

2-3

1-2

0-1

Bleeding [g/cm3]- predicted values incl. 10%-90% quantiles

gSH -. pSTpSH -. pSTpSH - .gSTgSH -. gSTFUTURE..eGRAVEL

WR30/35

AE30/35

N30/35

PAST…... . GRAVEL

0

2

4

6

8

10

12

14

168

The past gravel has higher bleeding values than the future gravel for all mix designs. The

crushed aggregate combinations exhibit bleeding phenomena similar to those of the past

gravel, except the good-shapes WR35, which has significantly higher bleeding. The

quantiles are also very large for the good-shaped WR35 mix design.

8.5 Predicting with the compressive strength model

8.5.1 SC- Los Angeles value and SC- flakiness 3.15/4.0 mm vs. compressive strength

Figure 112 shows the combined effect of the SC- Los Angeles value and SC- flakiness

3.15/4.0 mm on the compressive strength for the six mix designs. The ranges of the SC-

Los Angeles value and SC- flakiness are 19.6…38.2 % and 1.23…1.43 respectively. The

data and corresponding coefficient of variation values are presented in appendix 6.

The surfaces of the N and WR mix designs are controlled by changes in the SC- Los

Angeles value, and the influence of the SC- flakiness only marginally modifies the shape

of the 3D surface. The effects of the SC- flakiness are mainly due to the influence on the

workability, i.e. compaction degree of concrete, and strength properties of individual

aggregate particles. Both the N30 and WR30 have the lowest compressive strength

values with a combination of high SC- Los Angeles value and high SC- flakiness. In the

case of mix designs with the high paste amount the phenomenon is weaker. The WR mix

designs have the highest compressive strength values when both the SC- Los Angels and

SC- flakiness have low values. In contrast, the high paste N35 has the highest

compressive strength with a combination of low SC- Los Angels value and high SC-

flakiness. For the low paste N30 the effect of the SC- flakiness in the low SC- Los

Angeles value area is almost negligible. The gain of the compressive strength for the AE

mix designs is strongly dependent on the entrained air. Even so, high SC- Los Angeles

values do also affect the compressive strength of the AE mix designs negatively.

169

The range of the compressive strength values is the greatest for the WR mix designs and

the low paste N30 (table 51).

Table 51. Statistics for the predicted compressive strength for each mix design;

combined effect of the SC- Los Angeles value and SC- flakiness 3.15/4.0

mm

N30 N35 AE30 AE35 WR30 WR35

Min [MPa] 42 45 45 41 50 52

Max [MPa] 51 51 50 47 62 62

Average [MPa] 46 47 47 44 58 57

Stdev [MPa] 2.1 1.3 1.2 1.4 2.6 2.1

The quantiles of the expectation values are highest in the corners of the high SC- Los

Angeles value and low SC- flakiness as well as in those of low SC- Los Angeles value

and high SC- flakiness. Table 52 shows the coefficient of variation statistics for the

predicted compressive strength. More detailed information is given in appendix 6.

Table 52. Coefficient of variation statistics for the predicted compressive strength

value for each mix design;

combined effect of the SC- Los Angeles value and SC- flakiness 3.15/4.0

mm

N30 N35 AE30 AE35 WR30 WR35

Min [%] 2 2 2 2 2 2

Max [%] 8 8 8 9 8 9

Average [%] 3 3 4 4 3 3

Stdev [%] 1 1 1 1 1 2

170

Figure 112. Combined effect of the SC- Los Angeles value and

SC- flakiness 3.15/4.0 mm on the compressive strength; all mix designs

202224262830323436

38

1.231.28

1.32

1.36

1.41

42

44

46

48

50

52

Co

mp

. Str

eng

th [

MP

a]SC-LA value (mod.) [%]

SC-Flkn 3/4

C 91dN30

50-52

48-50

46-48

44-46

42-44

202224262830323436

38

1.231.28

1.32

1.36

1.41

42

44

46

48

50

52

Co

mp

. Str

eng

th [

MP

a]


SC-Flkn 3/4

C 91dN35

50-52

48-50

46-48

44-46

42-44

20222426283032343638

1.23

1.28

1.32

1.36

1.41

40

42

44

46

48

50

52

Co

mp

. Str

eng

th [

MP

a]


SC-Flkn 3/4

C 91dAE30

50-52

48-50

46-48

44-46

42-44

40-42

202224262830323436

38

1.231.28

1.32

1.36

1.41

40

42

44

46

48

50

52

Co

mp

. Str

eng

th [

MP

a]


SC-Flkn 3/4

C 91dAE35

50-52

48-50

46-48

44-46

42-44

40-42

202224262830323436

38

1.23

1.28

1.32

1.36

1.41

50

52

54

56

58

60

62

64

Co

mp

. Str

eng

th [

MP

a]


SC-Flkn 3/4

C 91dWR30

62-64

60-62

58-60

56-58

54-56

52-54

50-52

202224262830323436

38

1.23

1.28

1.32

1.36

1.41

50

52

54

56

58

60

62

64

Co

mp

. Str

eng

th [

MP

a]


SC-Flkn 3/4

C 91dWR35

62-64

60-62

58-60

56-58

54-56

52-54

50-52

171

8.5.2 SC- Los Angeles value and SC- pore area 60-300 Å vs. compressive strength

Figure 113 shows the combined effect of the SC- Los Angeles value and SC- flakiness

3.15/4.0 mm on the compressive strength for the six mix designs. The ranges of the SC-

pore area 60-300 Å and SC- Los Angeles value are 0…0.14 m2/g and 19.6…38.2%

respectively. The data and corresponding coefficient of variation values are presented in

appendix 6.

The N and AE mix designs have similar 3D surface shapes, whereas the WR mix designs

deviate fundamentally from their shape. The effect of the SC- pore area on the

compressive strength of the WR mix designs is minimal, and thus the combined effect of

the SC- pore area and SC- Los Angeles value is mainly dominated by the SC- Los

Angeles value changes. The highest and lowest predicted compressive strength values for

the WR mix designs are in the corners of low SC- pore area and low SC- Los Angeles

value and of high SC- pore area and high SC- Los Angeles value. For the N mix designs

the surface cambers approximately in the middle of the SC- pore area range, where the

compressive strength also reaches the highest value for each SC- Los Angeles value. The

AE mix designs camber similarly and also on the zero SC- pore area line, where the

compressive strength values are balanced between the strength of the aggregate (Los

Angeles value) and entrained air %.

The statistics for the predicted compressive strength values are presented in table 53. The

range of the compressive strength values is the greatest for the WR mix designs and the

low paste N30. The quantiles of the expectation values are highest in the high SC- pore

area. Table 54 shows the coefficient of variation statistics for the predicted compressive

strength. More detailed information is given in appendix 6.

172

Table 53. Statistics for the predicted compressive strength for each mix design;

combined effect of the SC- Los Angeles value and SC-pore area 60-300 Å

N30 N35 AE30 AE35 WR30 WR35

Min [MPa] 45 46 47 43 51 51

Max [MPa] 52 52 53 50 61 60

Average [MPa] 47 48 50 47 57 56

Stdev [MPa] 2.0 1.5 1.4 1.6 2.9 2.6

Table 54. Coefficient of variation statistics for the predicted compressive

strength value for each mix design;

combined effect of the SC- Los Angeles value and SC-pore area 60-300 Å

N30 N35 AE30 AE35 WR30 WR35

Min [%] 2 2 2 2 6 26

Max [%] 11 10 10 10 11 11

Average [%] 6 5 5 5 5 5

Stdev [%] 2 2 2 2 2 2

173

Figure 113. Combined effect of the SC- Los Angeles value and

SC- pore area 60-300 Å on the compressive strength; all mix designs

0.000.020.030.050.060.080.100.110.130.14

2024

28

32

36

44

46

48

50

52

54

Co

mp

. Str

eng

th [

MP

a]

SC-pore area 60-300Å [m2/g] SC-LA value (mod)

C 91dN30

52-54

50-52

48-50

46-48

44-46

0.000.020.030.050.060.080.100.110.130.14

2024

28

32

36

44

46

48

50

52

54

Co

mp

. Str

eng

th [

MP

a]


C 91dN35

52-54

50-52

48-50

46-48

44-46

0.000.020.030.050.060.080.100.110.130.14

20

24

28

32

36

42

44

46

48

50

52

54

Co

mp

. Str

eng

th [

MP

a]


SC-LA value (mod)

C 91dAE30

52-54

50-52

48-50

46-48

44-46

42-44

0.000.020.030.050.060.080.100.110.130.14

20

24

28

32

36

42

44

46

48

50

52

54

Co

mp

. Str

eng

th [

MP

a]


SC-LA value (mod)

C 91dAE35

52-54

50-52

48-50

46-48

44-46

42-44

0.000.020.030.050.060.080.100.110.130.14

2024

28

32

36

50

52

54

56

58

60

62

Co

mp

. Str

eng

th [

MP

a]


C 91dWR30

60-62

58-60

56-58

54-56

52-54

50-52

0.000.020.030.050.060.080.100.110.130.14

2024

28

32

36

50

52

54

56

58

60

62

Co

mp

. Str

eng

th [

MP

a]


C 91dWR35

60-62

58-60

56-58

54-56

52-54

50-52

174

8.5.3 Effect of different aggregate combinations on the compressive strength

Figure 114 presents predicted compressive strength values including 10% and 90%

quantiles for six different aggregate combinations. The combinations are described in

chapter 8.1.2.

Figure 114. Predicted compressive strength values for six different aggregate

combinations

There is no significant compressive strength difference between the past gravel, future

gravel and crushed rock product with good shape and good strength. If it has a good

strength, even concrete made of crushed rock product with a poor shape achieves a

quality similar to that of the N and AE mix designs, though some loss of compressive

strength occurs with the WR mix designs. A remarkable loss of compressive strength

ensues if the fine aggregate combination has both poor shape and poor strength. The

difference to PG is between 5 – 10 MPa for the N and AE mix designs and between 10-

15 MPa for the WR mix designs. If a product exhibiting poor strength is made with good

shape, the loss is prevented in the case of the N35 and AE mix designs. In the case of the

low paste N30 mix design some loss can be expected, and in the case of the WR mix

designs the loss is between 5-10 MPa.

Comp. strength [MPa] - predicted values incl. 10%-90% quantiles

gSH - pST.pSH - pST.pSH - gST.gSH - gST.

WR30/35

AE30/35

N30/35

FUTURE…GRAVEL

PAST…..…GRAVEL

35

40

45

50

55

60

65

175

8.6 Discussion of the predictions made with the models

The models were based on the outlines made with the fine aggregate and mix design

selections. The following considerations can be applied to the models:

• The selected fine aggregate products represent fairly well the common Finnish

variety range of concrete aggregates used in Finland, as well as representing products

that might replace the familiar fine aggregate solutions in the future, i.e. weathered

gravel and crushed rock products.

• All castings had the same grading curve (close to the ideal grading curve).

• The coarse aggregate was uncrushed gravel and was the same in all castings

• Castings were divided into six mix designs: cement amount 300 kg/m3 or 350 kg/m3,

no admixture w/c=0.65, superplasticizer w/c=0.58, air-entraining agent w/c=0.58

• The average compressive strength was 45 MPa – 55 MPa (no additives were used).

• The models were made for concrete with a maximum aggregate size of 14 mm

Table 56 presents a summary of the concrete properties made with the six aggregate

combinations (see chapter 8.1.2 and appendix 5). The points given to each aggregate-mix

design composition represent how much the concrete property value of the evaluated

aggregate combination deviated from the value attained with the past gravel. The rules

for the point scoring are shown in table 55. The deviations were calculated from the

expectation values.

Table 55. Rules for the point scoring for concrete property deviation between

the past gravel and the fine aggregate combination evaluated

Points per

deviation

Flow value

[mm]

Air %

[%]

Bleeding

[g/cm3]

Compressive strength

[MPa]

1 ± 50 ± 0.5 ± 2 ± 5

2 ± 100 ± 1.0 ± 4 ± 10

3 ± 150 ± 1.5 ± 6 ± 15

4 ± 200 ± 2.0 ± 8 ± 20

176

Table 56. Summary of the modelled concrete property values as

compared against the values for the past gravel

Fine aggregate combination

Future gravel

Good shape-

good strength

Poor shape-

good strength

Poor shape-

poor strength

Good shape-

poor strength

-1 -1 -3 -3 -1-1 0 -1 -1 0-1 -1 -2 -2 -1-1 0 -1 -1 0-2 -2 -4 -4 -2-2 0 -2 -2 0FL

OW

VA

LU

E

N30

N35

AE30

AE35

WR30

WR35

TOTAL -8 -4 -13 -13 -4-1 0 0 0 0-1 0 -1 -1 00 0 -2 -2 00 0 0 0 0-1 0 -2 -2 0-1 0 -1 -1 0

AIR

%

N30

N35

AE30

AE35

WR30

WR35

TOTAL -4 ±±±±0 -6 -6 ±±±±0+1 0 0 0 0+1 0 0 0 00 0 0 0 00 0 0 0 00 0 0 0 0

+1 -2 0 0 -2BL

EE

DIN

G

N30

N35

AE30

AE35

WR30

WR35

TOTAL +3 -2 ±±±±0 ±±±±0 -20 0 0 -2 00 0 0 -1 00 0 0 -1 00 0 0 -2 00 0 -1 -3 -20 0 0 -2 -1

CO

MPR

ESS

IVE

STR

EN

GT

H

N30

N35

AE30

AE35

WR30

WR35

TOTAL ±±±±0 ±±±±0 -1 -11 -3

177

If all six mix designs are included, the past gravel has by far the best workability, i.e.

flow value. The good-shaped rock, however, has equal workability if the amount of paste

is sufficient. On lower amounts of paste, the good-shaped rock has flow values equal to

those for the future gravel, which represents uncrushed gravel with weathering

properties. The loss in workability is considerable if the rock product is of poor shape,

and the influence, is strongest with the low paste mix designs.

The compressive strength values were same with the past gravel, future gravel and with

good-shape & good-strength rock. Even if the rock has poor shape, but if its strength is

good, the compressive strength values of the concrete are nearly equal to those of the past

gravel. Only some strength loss is expected with the WR30 mix design. If the aggregate,

on the other hand, has poor strength but a good shape, then the compressive strength loss

starts to build up, especially with the superplasticizer mix designs. If, however, both the

shape and strength are poor, then the compressive strength of the concrete is significantly

lower than that of the past gravel.

The future gravel and the good-shaped & good-strength rock both need extra water to

attain the same workability as the past gravel. Because there was no difference in the

compressive strength over the past gravel, any extra water added to increase the flow

value of the future gravel and of the good-shaped & good-strength rock leads to lower

compressive strength. We can thus conclude that even if the effective mix design

procedure would (most likely) raise the flow value of the future gravel to the same level

as the past gravel, it would in any case cause a reduction in the compressive strength.

Additionally, increasing the workability with cement paste will have economic

consequences, though to a much lesser degree than with the poor-shaped and good-

shaped & poor strength rock products. It is even uncertain if the same compressive

strength level as that of the past gravel can be reached, since both the workability and the

compressive strength are so greatly compromised.

The deviations in the air % are caused mainly by the low workability, and the

unsuccessful compaction and, for the AE mix designs, the unsuccessful air formation. It

178

is not possible to evaluate the quality of the entrained air on the basis of the amount of air

in the AE mix designs.

The future gravel, in general, reduces the bleeding tendency as a result of the particle

porosity characteristics. The good-shaped rock products, however, have a greatly

increased tendency for bleeding and even for segregation with the high paste concrete

with superplasticizer. This is also due to the particle porosity phenomenon, but in this

case the explanation is, more specifically, the lack of particle porosity.

Some of the concrete properties could be controlled by other mix design changes, e.g. by

adding additives and by changing the grading curve. One example is the workability of

the good-shaped rock product which, most likely, with a finer grading curve and/or

added additive could be adjusted without extra water to have a flow value equal to that of

the past gravel. The finest particles could reduce the friction between the semi-coarse

particles and to increase the packing density by filling the interstitial spaces between

them. This does not apply to the future gravel, as it has round/non-angular particles, but

anyhow worse workability than the past gravel.

The superplasticizer enhances the difference between the fine aggregate products, e.g. the

relative difference between the flow values of two fine aggregate combinations is greater

with the mix design containing the superplasticizer than without the admixture. This is

also the case with the compressive strength, though the strength level is higher with the

WR than with the N and AE mix designs; this, in turn, can influence the impact of the

inputs, e.g. the Los Angeles value. The superplasticizer also seems to affect how the

particle porosity characteristics affect the compressive strength. For the N and AE mix

designs there appears to be an optimum particle porosity level for the semi-coarse

fraction, whereas, no such point exists for the WR mix designs.

179

9. VERIFICATION OF THE MODELS WITH

TWO NEW AGGREGATE PRODUCTS

9.1 Procedure

The verification of the models was performed by introducing two such aggregate

products that had not been utilized in the modelling of the interaction between aggregate

characteristics and concrete properties. The aggregate products consisted of the fines and

semi-coarse fractions.

First the aggregate products were investigated with the same methods that are described

in the chapter 4.4 to obtain the input values for the models. Secondly, castings with the

mix designs N35, WR30 and AE35 were executed with both of the aggregate products.

Finally, the actual results were compared against the values attained from the models. In

the comparison is taken into account the repeatability error of the councrete

measurements (actual results) as well as the 10 % and 90 % quantiles for the modelled

values.

9.2 Identification of the new aggregate products

The aggregate input values of the two new aggregates are shown in table 57. The table

includes the aggregate characteristics for both the fines (F) and the semi-coarse (SC)

fractions. Additionally, the table presents which aggregate inputs are valid for each

model, e.g. flow value, air %, bleeding and compressive strength 91 d.

The A1 is a crushed rock and by rock type it is categorized as granite. The B16 is an

uncrushed granitic gravel.

180

Table 57. Aggregate input values of the B16 and A1 aggregate products

9.3 Results of the modelled and measured values

The results of the models include the expectation values and their 10% and 90 %

quantiles, whereas the measured values are presented by the actual values and their

repeatability ranges (chapter 6).

In each of the following figures the symbols are the same:

Asterisk ( * ) actual value

Box ( ) repeatability range of the actual value

Diamond ( ♦ ) expectation value (modelled value)

Line ( ) 10 –90 % quantiles of the expectation value

B16/B16 A1/A1 FLOW AIR % BLEEDING

COMP. STRENGTH 91d

BET 2,27 1,31 X X XF - density [Mg/m3] 2,7726 2,6711 XF - Zeta pot. [mV] -9,1 -11,2 X XF - Cu 6 14,6 X X XSC-avg. pore size [um] 1,74 9,72 XSC-tot. pore area [m2/g] 0,05 0,013 XSC-LA value (mod.) [%] 21,8 36,9 XSC-Elgn 3.15/4.0 mm 1,37 1,44 XSC-Elgn 1.6/2.0 mm 1,38 1,49 XSC-Elgn 0.8/1.0 mm 1,45 1,6 XSC-Angularity 4,9 7,14 X XSC-Qnty 1.6/2.0 mm 16610000 17000000 XSC-Flkn 3.15/4.0 mm 1,25 1,36 X X XSC-Flkn 1.6/2.0 mm 1,25 1,37 XF-mica [%] 1 3 XSC-pore area>900Å [m2/g] 0,022 0,009 XSC-pore area 300-900Å [m2/g] 0,005 0 X XSC-pore area 60-300Å [m2/g] 0,001 0 X X

181

Figure 115. Verification of the flow value model

Figure 116. Verification of the air % model

A1/A1 AE35

B16/B16 N35

B16/B16 WR30

B16/B16 AE35

A1/A1 WR30

A1/A1 N35

330 380 430 480 530 580 630

A1/A1 AE35

B16/B16 N35

B16/B16 WR30

B16/B16 AE35

A1/A1 WR30

A1/A1 N35

0 1 2 3 4 5 6 7 8

Air [ % ]

Flow value [mm]

182

Figure 117. Verification of the bleeding model

Figure 118. Verification of the compressive strength model

A1/A1 AE35

B16/B16 N35

B16/B16 WR30

B16/B16 AE35

A1/A1 WR30

A1/A1 N35

38 43 48 53 58 63 68

A1/A1 AE35

B16/B16 N35

B16/B16 WR30

B16/B16 AE35

A1/A1 WR30

A1/A1 N35

0 1 2 3 4 5 6 7 8

Compressive strength 91d [ MPa]

Bleeding [ g/cm3]

183

9.4 Evaluation of the verification of the modelled and measured values

The evaluation of the models is carried out by comparing the modelled and measured

values and by detecting if the ranges of the 10 – 90 % quantiles and repeatability are

overlapping. If the modelled value is inside the repeatability range, it can be stated that

the modelled value is within the 95 % confidence interval of the measurement. If instead

only the repetabilty range and the quantiles of the 10 – 90 % overlap, then can be stated

that due to the model error there exists a statistical probability that the modelled and

measured values are equal. However, that cannot be statistically proved.

Table 58. Evaluation of the accuracy of the modelled and measured values for

the verification castings (N35, WR30 and AE35)

Modelled value inside the

measurement repeatability

Measurement repeatability

and quantiles of the model

overlap

B16 A1 B16 A1

Flow value 2 of 3 1 of 3 1 of 3 2 of 3

Air % 1 of 3 2 of 3 2 of 3 1 of 3

Bleeding 2 of 3 2 of 3 1 of 3 1 of 3

Compressive strength 91 d 2 of 3 1 of 3 1 of 3 2 of 3

As a conclusion one can say that the model predicted the actual behaviour of uncrushed

gravel B16 very well and the crushed rock A1 satisfactorily.

184

10. CONCLUSIONS

The results for aggregate and concrete as well as the results for the models are based on

21 aggregate products and 215 castings made with six different mix designs. The mix

designs consisted of two cement amounts (300 or 350 kg/m3) corresponding to low and

high paste volumes, with three admixture possibilities: no admixture, superplasticizer or

air-entraining agent.

The following listing of the conclusions is divided into seven headlines. The four first

ones are the modelled concrete parameters; flow value, compressive strength, air % and

bleeding. The three next groups are the drying shrinkage, the aggregate characteristics

and the aggregate testing methods.

FLOW VALUE

1. For the same mix design, the flow value differed according to the quality

characteristics of the fine aggregate, and ranged between 150...295 mm, i.e.

2.5…5 workability classes. The greater variations were observed with the low

paste mix designs. (concrete test results)

2. The most important fine aggregate characteristics in the flow value model were

the semi-coarse fraction parameters related to shape and particle porosity. Even

though the amount of paste was the decisive parameter in determining the general

level of the flow value, the effect of the fine aggregate characteristics could

exceed the effect of the paste difference. For the no-admixture and air-entraining

agent mix designs, the average effect of the paste difference was 100 mm, and for

the superplasticizer mix designs it was 200 mm. The flow model error was 34

(32-38) mm and the repeatability error of the measurement represents 44% of the

model error. (model)

3. The weathering of gravel affected the flow value more negatively than the use of

a combination of good-shaped rock as the semi-coarse fraction and unweathered

filler aggregate as the fines. If the shape of the rock semi-coarse fraction was

poor, the reduction observed in the flow value was extensive, especially if the

paste amount was low. For the low paste mix designs the reduction was up to

185

100…200 mm, i.e. 1.5…3.5 workability classes, dependent of the mix design and

for the high paste amount the reduction was up to 50…100 mm.

(model/sensitivity analysis, predictions with the model)

4. The superplasticizer enhanced the difference in quality between fine aggregate

products. The relative difference between the flow values of two fine aggregate

products was greater with superplasticizer mix designs than with mix designs

without any admixture or with the air-entraining agent. (concrete test results &

predictions with the model)

COMPRESSIVE STRENGTH

5. For the same mix design, the compressive strength differed according to the

quality characteristics of the fine aggregate, and ranged between 8.4…18.8 MPa.

The largest variation was detected with the superplasticizer mix designs. The

average compressive strength of the mix designs varied between approximately

45 – 55 MPa. (concrete test results)

6. The most important fine aggregate characteristics for the compressive strength

model were the flakiness 3.15/4.0 mm and the Los Angeles value of the semi-

coarse fraction. The compressive strength model error was 2.7 (2.4-3.0) MPa, and

the repeatability error of the measurement represents 78% of the model error.

(model)

7. If the rock product was good-shaped and had good strength, then the compressive

strength of the concrete equalled that of the unweathered gravel. Even if the rock

had poor shape, if it had good strength the compressive strength was only

marginally affected. With the combination of poor strength and good shape, the

compressive strength with the superplasticizer mix designs was reduced by 5-10

MPa as compared to the unweathered gravel. (predictions with the model)

8. If the rock product had the combined characteristics of poor shape, i.e. flaky/thin

particles, and poor strength, the quality characteristics had a manifold negative

effect on the compressive strength. The compressive strength was reduced by as

much as 10-15 MPa with the superplasticizer mix designs and by 5-10 MPa with

mix designs having no admixture or having air-entraining agent (predictions with

the model)

186

9. The superplasticizer affected the bond between the aggregate particles and the

paste (see #8). The effect of the changes on the Los Angeles value was the

strongest with the superplasticizer mix designs as compared to mix designs

without any admixture or with the air-entraining agent. (model/sensitivity

analysis, predictions with the model)

10. For the no admixture and air-entraining agent mix designs, there was an optimum

particle porosity level for the semi-coarse fraction when the compressive strength

was the highest, but for the superplasticizer mix designs the effect of the particle

porosity of the semi-coarse fraction was linear. (predictions with the model)

AIR %

11. For same mix design, the air % differed according to the quality characteristics of

the fine aggregate and ranged between 1.1…1.8% for the no admixture and

superplasticizer mix designs and 4.5% for the air-entraining agent mix designs.

(concrete test results)

12. The amount of the entrapped and entrained air correlated strongly with the flow

value. Thus, the most important fine aggregate characteristics were the particle

porosity and shape related semi-coarse fraction parameters. The air % model

error was 0.6% (0.5-0.7%) and the repeatability error of the measurement

represents 50% of the model error. (model)

BLEEDING

13. For same mix design, the bleeding varied due to the quality characteristics of the

fine aggregate and ranged between 2.2…8.7 g/cm3 for all other mix designs

except for the high paste superplasticizer mix design, which had variation of 18.2

g/cm3. (concrete test results)

14. The most important parameters for the bleeding model were the superplasticizer

and the amount of paste. From the fine aggregate characteristics, the particle

porosity parameters of the semi-coarse fraction and surface area parameter for the

fines were important. Additionally, the shape parameters of the semi-coarse

fraction were important for the model, as they decreased the workability and thus

reduced the bleeding tendency. The bleeding model error was 1.8 (1.3-2.2) g/cm3

187

and the repeatability error of the measurement represents 72% of the model error.

(model)

15. The fine aggregate combination of good-shaped rock as the semi-coarse fraction

and unweathered filler aggregate as fines, greatly increased the tendency for

bleeding and even for segregation with the high paste mix design with

superplasticizer, as the combination doesn’t have the water restraining effect of

the particle porosity and surface area. (model/sensitivity analysis, predictions with

the model)

DRYING SHRINKAGE

16. The effect of the weathering properties of the fine aggregate on the drying

shrinkage exceeded twice the effect of the mix design changes, i.e. amount of

paste and/or the admixtures. Based on the results can be concluded, that the

drying shrinkage caused by the weathering properties is partly self-induced; as the

water absorption reduces the workability, extra water is needed for replacement,

which in turn, together with the time-dependent water evaporation from the

aggregate pores, enhances the shrinkage additionally. (concrete test results)

AGGREGATE CHARACTERISTICS

17. In general, the gravel products are more likely to have weathering properties in

both the semi-coarse fraction and fines than in the rock products. Additionally,

the fineness of the fines can vary to a great extent. The rock products, on the other

hand, are always more elongated, flaky and angular than the gravel products. The

shape properties can vary significantly, as can the resistance to fragmentation and

the particle density. Furthermore, the fineness of the fines can vary considerably.

Partly crushed gravel and a mixture of uncrushed gravel and rock are thus likely

to have combinations of the rock and uncrushed gravel properties. (aggregate test

result)

AGGREGATE TESTING METHODS

18. By applying the developed shape determination method, it is possible to obtain

totally independent values for the elongation and flakiness. (aggregate test

results)

188

10. NEED FOR FUTURE RESEARCH

The models for the prediction of concrete properties developed in this study cover the

influence of fine aggregate in normal, textbook mix designs.

Thus, the following future research areas can be detected:

AGGREGATE / BASIC CHARACTERISTICS

1. Influence of the coarse aggregate

AGGREGATE PRODUCTS

2. Influence of different grading curves

MIX DESIGNS

3. Special concrete mix designs, e.g. self-compacting concrete and high strength

concrete

189

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APPENDIX 1

ORIGINAL GRADINGS OF THE STUDIED AGGREGATE PRODUCTS

ROCK PRODUCTS

A2 A3 A6 A7 A8 A10 A15 A16Sieve [mm] Passing-%

0,063 4,4 5,1 5,7 3,0 4,9 6,4 9,3 8,30,125 8 9 11 6 10 15 12 14

0,25 14 14 22 8 18 31 15 210,5 23 21 36 12 30 47 20 28

1 36 31 58 17 45 63 28 362 55 45 82 29 69 79 46 494 79 66 93 51 98 96 77 698 100 96 98 82 100 100 100 97

16 100 100 100 100 100 100 100 100

GRAVEL PRODUCTS

B1 B2 B3 B6 B7 B8 B9 B10 B11 B12 B13 B14Sieve [mm] Passing-%

0,063 0,5 2,9 15,9 1,8 3,5 1,6 2,9 1,2 2,7 2,9 2,2 2,00,125 2 8 51 3 7 3 14 3 5 6 5 8

0,25 4 21 85 4 13 7 53 9 11 12 12 230,5 13 47 95 6 24 21 91 21 25 22 23 41

1 33 65 97 12 36 52 98 37 38 36 41 582 60 79 99 33 52 81 100 60 56 55 63 744 82 90 99 67 70 93 100 81 78 74 82 888 96 98 100 99 95 99 100 97 94 98 99 97

16 100 100 100 100 100 100 100 100 100 100 100 100

LIST OF CASTINGS AND REPETITIONS APPENDIX 2 1/4

SEM NA AE WR RES FINES 1 FINES2 FINES3 FINES4 COARSE1 COARSE2 COARSE3 COARSE4 Repetitions

350 1 0 0 1 B13 1 0 0 0 0 0 0 B11 0,5 A7 0,5 0 0 0 0300 0 1 0 2 B3 0,25 A2 0,75 0 0 0 0 B2 0,75 A8 0,25 0 0 0 0300 1 0 0 3 B3 0,5 A2 0,5 0 0 0 0 B13 1 0 0 0 0 0 0350 0 0 1 4 B13 1 0 0 0 0 0 0 B8 0,5 A7 0,5 0 0 0 0300 0 0 1 5 B7 1 0 0 0 0 0 0 A3 1 0 0 0 0 0 0300 0 1 0 6 B10 0,25 A15 0,75 0 0 0 0 B2 0,5 A2 0,5 0 0 0 0300 1 0 0 7 B7 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 39350 0 0 1 8 B10 0,25 A7 0,75 0 0 0 0 B7 1 0 0 0 0 0 0300 1 0 0 9 B3 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0350 1 0 0 10 B6 0,5 B10 0,25 B12 0,25 0 0 B2 1 0 0 0 0 0 0350 0 1 0 11 B1 0,25 A2 0,75 0 0 0 0 B7 0,5 A3 0,5 0 0 0 0350 1 0 0 12 B1 0,5 A8 0,5 0 0 0 0 A2 1 0 0 0 0 0 0300 1 0 0 13 A3 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0350 0 0 1 14 B3 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 15 B1 0,75 A15 0,25 0 0 0 0 A2 1 0 0 0 0 0 0300 0 0 1 16 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 195350 1 0 0 17 B2 0,5 B3 0,5 0 0 0 0 REF 1 0 0 0 0 0 0300 1 0 0 18 B7 0,25 A16 0,75 0 0 0 0 B7 0,5 A16 0,5 0 0 0 0300 0 0 1 19 B12 0,75 B14 0,25 0 0 0 0 B13 1 0 0 0 0 0 0300 1 0 0 20 B10 0,5 B13 0,5 0 0 0 0 REF 0,5 A8 0,5 0 0 0 0300 1 0 0 21 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 35, 175350 0 0 1 22 B3 1 0 0 0 0 0 0 A7 1 0 0 0 0 0 0300 1 0 0 23 B7 0,25 B12 0,5 A3 0,25 0 0 REF 1 0 0 0 0 0 0350 1 0 0 24 B3 0,5 B11 0,5 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 25 B1 1 0 0 0 0 0 0 A7 1 0 0 0 0 0 0350 0 1 0 26 B9 0,5 A3 0,5 0 0 0 0 B7 0,5 A2 0,5 0 0 0 0 60, 66, 89, 140300 1 0 0 27 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 42, 74, 112, 155, 221300 1 0 0 28 B7 0,25 B12 0,5 A16 0,25 0 0 A16 1 0 0 0 0 0 0350 1 0 0 29 B7 1 0 0 0 0 0 0 A2 1 0 0 0 0 0 0 150300 0 1 0 30 B7 0,5 A6 0,5 0 0 0 0 B2 0,5 A8 0,5 0 0 0 0350 0 1 0 31 A8 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0350 1 0 0 32 B9 0,75 A8 0,25 0 0 0 0 B8 1 0 0 0 0 0 0300 1 0 0 33 B7 0,5 B9 0,5 0 0 0 0 REF 0,5 A8 0,5 0 0 0 0 67, 87300 0 1 0 34 B8 0,25 B11 0,5 A16 0,25 0 0 REF 0,5 A16 0,5 0 0 0 0300 1 0 0 35 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 *350 0 0 1 36 B2 0,5 A6 0,5 0 0 0 0 REF 1 0 0 0 0 0 0350 0 0 1 37 B1 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0300 1 0 0 38 B3 1 0 0 0 0 0 0 A10 1 0 0 0 0 0 0300 1 0 0 39 B7 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 *350 1 0 0 40 A2 0,75 A8 0,25 0 0 0 0 B7 1 0 0 0 0 0 0350 0 0 1 41 B7 0,25 B12 0,75 0 0 0 0 REF 1 0 0 0 0 0 0 106, 244300 1 0 0 42 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *300 0 0 1 43 B7 0,75 A7 0,25 0 0 0 0 B13 1 0 0 0 0 0 0300 1 0 0 44 B6 0,5 A6 0,5 0 0 0 0 B8 1 0 0 0 0 0 0350 0 0 1 45 B14 0,25 B11 0,75 0 0 0 0 REF 1 0 0 0 0 0 0350 0 0 1 46 B7 0,5 A16 0,5 0 0 0 0 B7 0,5 B13 0,5 0 0 0 0300 0 1 0 47 B13 0,25 A6 0,75 0 0 0 0 B7 1 0 0 0 0 0 0350 0 1 0 48 B2 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 114300 0 0 1 49 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 103350 0 1 0 50 B8 0,5 B11 0,5 0 0 0 0 REF 1 0 0 0 0 0 0 116300 0 1 0 51 B2 0,5 A16 0,5 0 0 0 0 A10 1 0 0 0 0 0 0300 1 0 0 52 B2 0,25 B7 0,25 B8 0,25 B11 0,25 REF 0,5 B13 0,5 0 0 0 0300 0 0 1 53 B6 0,5 B12 0,25 A15 0,25 0 0 A10 1 0 0 0 0 0 0 160



300 0 0 1 54 B14 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0350 0 1 0 55 B3 0,25 A8 0,75 0 0 0 0 B13 1 0 0 0 0 0350 0 0 1 56 B3 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0350 0 0 1 57 B7 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0 161350 0 1 0 58 B7 1 0 0 0 0 0 0 B7 1 0 0 0 0 0 0 64300 0 0 1 59 B11 1 0 0 0 0 0 0 B7 1 0 0 0 0 0 0350 0 1 0 60 B9 0,5 A3 0,5 0 0 0 0 B7 0,5 A2 0,5 0 0 0 0 *350 1 0 0 61 B7 0,25 B8 0,5 A2 0,25 0 0 REF 0,5 A10 0,5 0 0 0 0350 0 0 1 62 B13 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 63 B3 1 0 0 0 0 0 0 A3 1 0 0 0 0 0 0 108350 0 1 0 64 B7 1 0 0 0 0 0 0 B7 1 0 0 0 0 0 0 *300 0 1 0 65 B2 1 0 0 0 0 0 0 A3 0,5 A16 0,5 0 0 0 0350 0 1 0 66 B9 0,5 A3 0,5 0 0 0 0 B7 0,5 A2 0,5 0 0 0 0 *300 1 0 0 67 B7 0,5 B9 0,5 0 0 0 0 REF 0,5 A8 0,5 0 0 0 0 *350 0 0 1 68 B2 0,5 A2 0,5 0 0 0 0 B14 0,5 A3 0,5 0 0 0 0350 0 1 0 69 B2 0,5 A8 0,5 0 0 0 0 A8 0,5 A10 0,5 0 0 0 0350 0 1 0 70 B12 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 71 B14 0,25 A6 0,75 0 0 0 0 REF 1 0 0 0 0 0 0300 0 1 0 72 B6 0,25 B11 0,25 A7 0,5 0 0 REF 1 0 0 0 0 0 0300 0 0 1 73 B9 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0300 1 0 0 74 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 0 1 75 B1 1 0 0 0 0 0 0 B14 0,5 A16 0,5 0 0 0 0350 0 1 0 76 B7 0,5 A3 0,5 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 77 B1 1 0 0 0 0 0 0 A2 1 0 0 0 0 0 0350 0 0 1 78 B1 0,25 B10 0,5 A15 0,25 0 0 A7 0,25 A8 0,5 A10 0,25 0 0350 1 0 0 79 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0300 0 1 0 80 B14 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0350 1 0 0 81 B3 0,5 A15 0,5 0 0 0 0 REF 1 0 0 0 0 0 0350 1 0 0 82 B2 0,5 A8 0,5 0 0 0 0 B7 1 0 0 0 0 0 0350 0 0 1 83 B9 0,5 B12 0,5 0 0 0 0 B7 1 0 0 0 0 0 0300 1 0 0 84 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0 215300 0 1 0 85 B3 0,75 A16 0,25 0 0 0 0 REF 1 0 0 0 0 0 0300 0 1 0 86 B3 0,25 A15 0,75 0 0 0 0 B8 0,5 A16 0,5 0 0 0 0300 1 0 0 87 B7 0,5 B9 0,5 0 0 0 0 REF 0,5 A8 0,5 0 0 0 0 *350 1 0 0 88 B1 0,25 B9 0,25 A15 0,5 0 0 B14 0,5 A3 0,5 0 0 0 0350 0 1 0 89 B9 0,5 A3 0,5 0 0 0 0 B7 0,5 A2 0,5 0 0 0 0 *350 1 0 0 90 B2 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 91 B6 0,25 B7 0,25 B13 0,25 B14 0,25 A8 0,5 A10 0,5 0 0 0 0300 0 1 0 92 B8 0,5 A16 0,5 0 0 0 0 B13 1 0 0 0 0 0 0300 0 0 1 93 B3 0,5 B8 0,25 A6 0,25 0 0 B7 1 0 0 0 0 0 0 95300 0 0 1 94 B1 0,25 B10 0,25 B11 0,25 A8 0,25 REF 1 0 0 0 0 0 0300 0 0 1 95 B3 0,5 B8 0,25 A6 0,25 0 0 B7 1 0 0 0 0 0 0 *350 1 0 0 96 B11 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 0 1 97 B14 0,75 B11 0,25 0 0 0 0 REF 1 0 0 0 0 0 0300 1 0 0 99 B10 0,5 B11 0,25 A15 0,25 0 0 A7 0,5 A8 0,5 0 0 0 0300 0 1 0 100 B1 0,5 B3 0,5 0 0 0 0 B1 1 0 0 0 0 0 0 102300 0 1 0 102 B1 0,5 B3 0,5 0 0 0 0 B1 1 0 0 0 0 0 0 *300 0 0 1 103 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 0 1 106 B7 0,25 B12 0,75 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 0 1 107 A8 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 1 0 108 B3 1 0 0 0 0 0 0 A3 1 0 0 0 0 0 0 *350 1 0 0 109 B12 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0350 1 0 0 110 B1 0,5 A8 0,5 0 0 0 0 A7 0,5 A8 0,5 0 0 0 0300 0 1 0 111 B3 0,5 A7 0,5 0 0 0 0 B1 1 0 0 0 0 0 0



300 1 0 0 112 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *300 0 0 1 113 B9 0,25 A3 0,75 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 114 B2 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *300 0 1 0 115 B3 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0 146350 0 1 0 116 B8 0,5 B11 0,5 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 1 0 117 B9 1 0 0 0 0 0 0 B8 0,5 A8 0,5 0 0 0 0300 1 0 0 118 B1 0,25 B3 0,25 B14 0,25 B11 0,25 A7 0,5 A16 0,5 0 0 0 0350 0 1 0 120 B8 0,25 B11 0,5 A16 0,25 0 0 REF 0,5 A16 0,5 0 0 0 0350 0 1 0 121 B11 0,5 A15 0,5 0 0 0 0 REF 0,5 A8 0,5 0 0 0 0300 0 1 0 122 B3 0,5 B9 0,5 0 0 0 0 A16 1 0 0 0 0 0 0300 1 0 0 123 B9 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0350 1 0 0 125 B13 0,25 A3 0,75 0 0 0 0 B13 0,5 A16 0,5 0 0 0 0300 0 0 1 126 B14 0,25 A8 0,75 0 0 0 0 REF 1 0 0 0 0 0 0350 1 0 0 127 B3 0,25 A15 0,75 0 0 0 0 A10 1 0 0 0 0 0 0350 1 0 0 129 B3 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0350 1 0 0 130 B10 0,25 B11 0,25 A15 0,25 A16 0,25 A7 0,25 A8 0,25 A10 0,25 A16 0,25300 0 1 0 131 B7 0,25 B13 0,5 A8 0,25 0 0 REF 1 0 0 0 0 0 0300 0 1 0 132 B1 0,25 B10 0,75 0 0 0 0 REF 1 0 0 0 0 0 0300 0 0 1 133 B3 0,5 B11 0,5 0 0 0 0 B8 1 0 0 0 0 0 0350 0 0 1 134 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 229350 0 0 1 136 B9 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0300 1 0 0 137 B11 0,25 A6 0,75 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 138 B10 0,25 B12 0,75 0 0 0 0 B12 1 0 0 0 0 0 0300 0 1 0 139 B9 0,75 A8 0,25 0 0 0 0 B8 1 0 0 0 0 0 0350 0 1 0 140 B9 0,5 A3 0,5 0 0 0 0 B7 0,5 A2 0,5 0 0 0 0 *300 0 1 0 141 B9 0,5 A7 0,5 0 0 0 0 B11 1 0 0 0 0 0 0300 0 0 1 142 B1 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0350 1 0 0 143 B11 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0300 1 0 0 144 B14 1 0 0 0 0 0 0 B8 1 0 0 0 0 0 0350 0 0 1 145 B13 0,25 B11 0,5 A3 0,25 0 0 REF 0,5 A7 0,5 0 0 0 0300 0 1 0 146 B3 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0 *300 0 0 1 147 B3 0,25 B6 0,25 B13 0,25 B11 0,25 REF 1 0 0 0 0 0 0350 0 0 1 148 B3 0,5 A7 0,5 0 0 0 0 A3 1 0 0 0 0 0 0300 0 1 0 149 B12 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 150 B7 1 0 0 0 0 0 0 A2 1 0 0 0 0 0 0 *300 0 0 1 151 B9 0,5 A6 0,5 0 0 0 0 B13 1 0 0 0 0 0 0300 0 1 0 152 B11 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0350 0 1 0 153 B14 0,5 A7 0,5 0 0 0 0 REF 0,5 A7 0,5 0 0 0 0300 1 0 0 155 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *300 0 1 0 156 B8 0,25 A16 0,75 0 0 0 0 B1 1 0 0 0 0 0 0300 0 1 0 158 B13 0,5 A3 0,5 0 0 0 0 B11 1 0 0 0 0 0 0350 0 1 0 159 B11 1 0 0 0 0 0 0 A2 0,5 A8 0,5 0 0 0 0300 0 0 1 160 B6 0,5 B12 0,25 A15 0,25 0 0 A10 1 0 0 0 0 0 0 *350 0 0 1 161 B7 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0 *350 1 0 0 162 B12 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0300 0 0 1 163 B11 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0300 0 0 1 164 B3 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0 185350 0 1 0 165 B3 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 166 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0350 1 0 0 167 A8 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 1 0 168 A16 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0300 0 0 1 169 B10 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0350 1 0 0 170 B14 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0350 0 1 0 171 A15 1 0 0 0 0 0 0 B8 1 0 0 0 0 0 0



350 0 1 0 172 B11 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0300 0 0 1 173 B12 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0350 1 0 0 174 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0300 1 0 0 175 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 *350 0 1 0 176 B3 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0300 0 0 1 177 A16 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0350 1 0 0 178 B3 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 1 0 179 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0350 0 0 1 180 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0300 0 0 1 181 B8 1 0 0 0 0 0 0 B8 1 0 0 0 0 0 0350 0 1 0 182 A8 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0300 1 0 0 183 A16 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0350 1 0 0 184 B8 1 0 0 0 0 0 0 B8 1 0 0 0 0 0 0300 0 0 1 185 B3 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0 *300 0 1 0 186 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0300 0 1 0 187 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0300 0 0 1 188 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0350 0 1 0 189 A6 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0350 1 0 0 190 B11 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0350 0 1 0 191 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 1 0 192 B8 1 0 0 0 0 0 0 B8 1 0 0 0 0 0 0350 1 0 0 193 A16 1 0 0 0 0 0 0 A16 1 0 0 0 0 0 0300 0 0 1 194 B10 1 0 0 0 0 0 0 A10 1 0 0 0 0 0 0300 0 0 1 195 B2 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0 *300 1 0 0 196 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0300 0 0 1 197 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 1 0 0 198 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0300 0 0 1 199 A6 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0 243350 0 1 0 200 B12 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0 242350 0 0 1 201 B11 1 0 0 0 0 0 0 B11 1 0 0 0 0 0 0350 1 0 0 202 B10 1 0 0 0 0 0 0 A10 1 0 0 0 0 0 0350 0 1 0 203 A16 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0300 0 0 1 204 B3 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 0 1 205 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0350 0 1 0 206 A8 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0350 0 1 0 207 B10 1 0 0 0 0 0 0 A10 1 0 0 0 0 0 0350 1 0 0 208 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0300 0 0 1 209 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0350 1 0 0 210 A6 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0300 0 0 1 211 A16 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0350 1 0 0 212 A16 1 0 0 0 0 0 0 B2 1 0 0 0 0 0 0350 0 1 0 213 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0350 0 0 1 214 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0300 1 0 0 215 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0 *300 0 1 0 216 B1 1 0 0 0 0 0 0 B1 1 0 0 0 0 0 0300 1 0 0 217 B13 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0350 1 0 0 218 B3 1 0 0 0 0 0 0 A8 1 0 0 0 0 0 0300 1 0 0 221 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 0 1 222 B3 1 0 0 0 0 0 0 B13 1 0 0 0 0 0 0350 0 0 1 225 B3 1 0 0 0 0 0 0 A10 1 0 0 0 0 0 0350 0 0 1 229 B3 1 0 0 0 0 0 0 REF 1 0 0 0 0 0 0 *350 0 1 0 242 B12 1 0 0 0 0 0 0 B12 1 0 0 0 0 0 0 *300 0 0 1 243 A6 1 0 0 0 0 0 0 B14 1 0 0 0 0 0 0 *350 0 0 1 244 B7 0,25 B12 0,75 0 0 0 0 REF 1 0 0 0 0 0 0 *

CORRELATIONS WITHIN THE FINES INPUTS APPENDIX 31/2

BET

value

Density Hf Sieving

63/125

Share %

crushed

Average

pore size

Total pore area

ZETA

potential

% MICA

fines

Passing -% 8 microns

Cu LD surface area

BET value 1,00 -0,05 0,51 0,09 -0,38 -0,68 0,97 0,07 0,02 0,53 0,51 0,43Density -0,05 1,00 0,46 0,61 0,60 -0,24 0,00 0,05 0,40 0,38 0,47 0,44Hf 0,51 0,46 1,00 0,74 0,21 -0,80 0,64 0,00 0,09 0,99 0,92 0,94Sieving 63/125 0,09 0,61 0,74 1,00 0,48 -0,49 0,23 -0,03 0,36 0,66 0,65 0,61Share % crushed -0,38 0,60 0,21 0,48 1,00 0,04 -0,29 -0,04 0,22 0,17 0,25 0,16Average pore size -0,68 -0,24 -0,80 -0,49 0,04 1,00 -0,78 0,01 -0,32 -0,78 -0,86 -0,65Total pore area 0,97 0,00 0,64 0,23 -0,29 -0,78 1,00 0,02 0,03 0,67 0,62 0,56ZETA potential 0,07 0,05 0,00 -0,03 -0,04 0,01 0,02 1,00 -0,04 -0,01 0,01 0,02% MICA fines 0,02 0,40 0,09 0,36 0,22 -0,32 0,03 -0,04 1,00 0,01 0,29 -0,15Passing -% 0.008 mm 0,53 0,38 0,99 0,66 0,17 -0,78 0,67 -0,01 0,01 1,00 0,91 0,95Cu 0,51 0,47 0,92 0,65 0,25 -0,86 0,62 0,01 0,29 0,91 1,00 0,80LD surface area 0,43 0,44 0,94 0,61 0,16 -0,65 0,56 0,02 -0,15 0,95 0,80 1,00

CORRELATIONS WITHIN THE SEMI-COARSE INPUTS APPENDIX 32/2

Density Optiroc Mica

layers

Average

pore size

Tot. pore

area

Pore area

>900Å

Pore area

>300Å

Pore area

300-900Å

Pore area

60-300Å

Surface

Texture

Los

Angeles

Share %

crushedDensity 1,00 -0,08 0,20 0,12 -0,25 -0,10 -0,24 -0,29 -0,24 0,65 0,07 0,49Optiroc -0,08 1,00 -0,18 -0,03 -0,26 -0,31 -0,31 -0,27 -0,17 -0,57 -0,30 -0,31Mica layers 0,20 -0,18 1,00 0,76 -0,30 -0,39 -0,35 -0,27 -0,20 0,17 0,78 0,65Average pore size 0,12 -0,03 0,76 1,00 -0,66 -0,70 -0,72 -0,63 -0,50 -0,15 0,74 0,70Tot. pore area -0,25 -0,26 -0,30 -0,66 1,00 0,71 0,97 0,99 0,94 0,32 -0,24 -0,41Pore area >900Å -0,10 -0,31 -0,39 -0,70 0,71 1,00 0,85 0,63 0,45 0,42 -0,20 -0,36Pore area >300Å -0,24 -0,31 -0,35 -0,72 0,97 0,85 1,00 0,94 0,83 0,35 -0,26 -0,44Pore area 300-900Å -0,29 -0,27 -0,27 -0,63 0,99 0,63 0,94 1,00 0,95 0,26 -0,26 -0,43Pore area 60-300Å -0,24 -0,17 -0,20 -0,50 0,94 0,45 0,83 0,95 1,00 0,24 -0,19 -0,33Surface Texture 0,65 -0,57 0,17 -0,15 0,32 0,42 0,35 0,26 0,24 1,00 0,09 0,41LosAngeles 0,07 -0,30 0,78 0,74 -0,24 -0,20 -0,26 -0,26 -0,19 0,09 1,00 0,68Share % crushed 0,49 -0,31 0,65 0,70 -0,41 -0,36 -0,44 -0,43 -0,33 0,41 0,68 1,00elongation 3.15/4.0 0,56 -0,39 0,29 0,37 -0,40 -0,19 -0,39 -0,46 -0,37 0,51 0,42 0,77elongation 1.6/2.0 0,50 -0,33 0,45 0,53 -0,47 -0,30 -0,48 -0,52 -0,40 0,45 0,50 0,78elongation 0.8/1.0 0,44 -0,42 0,46 0,61 -0,47 -0,32 -0,47 -0,49 -0,43 0,42 0,55 0,94Angularity 0,53 -0,24 0,78 0,74 -0,47 -0,41 -0,51 -0,49 -0,38 0,41 0,74 0,93Qnty 3.15/4.0 0,35 -0,32 0,75 0,64 -0,38 -0,30 -0,38 -0,38 -0,33 0,31 0,63 0,70Qnty 1.6/2.0 0,17 -0,25 0,56 0,42 -0,25 -0,14 -0,22 -0,24 -0,27 0,18 0,42 0,48Qnty 0.8/1.0 0,39 -0,25 0,73 0,62 -0,41 -0,31 -0,41 -0,41 -0,37 0,28 0,60 0,64Surface 3.15/4.0 0,24 -0,13 0,44 0,36 -0,24 -0,19 -0,24 -0,24 -0,22 0,15 0,31 0,34Surface 1.6/2.0 0,11 -0,04 0,26 0,18 -0,14 -0,08 -0,12 -0,13 -0,15 0,04 0,13 0,14Surface 0.8/1.0 0,23 -0,07 0,39 0,32 -0,24 -0,18 -0,24 -0,24 -0,22 0,11 0,26 0,27Tot. surface 0,20 -0,08 0,37 0,29 -0,21 -0,16 -0,21 -0,21 -0,20 0,10 0,24 0,25flakiness 3.15/4.0 0,50 -0,43 0,62 0,60 -0,44 -0,28 -0,44 -0,47 -0,40 0,48 0,64 0,88flakiness 1.6/2.0 0,46 -0,40 0,59 0,61 -0,47 -0,29 -0,47 -0,51 -0,43 0,46 0,60 0,87flakiness 0.8/1.0 0,46 -0,42 0,65 0,70 -0,49 -0,35 -0,49 -0,51 -0,44 0,43 0,68 0,95

Elgn 3.15/4.0

Elgn 1.6/2.0

Elgn 0.8/1.0

Angularity Qnty 3.15/4.0

Qnty 1.6/2.0

Qnty 0.8/1.0

Surface 3.15/4.0

Surface 1.6/2.0

Surface 0.8/1.0

Tot. surface

Flk 3.15/4.0

Flk 1.6/2.0

Flk 0.8/1.0

Density 0,56 0,50 0,44 0,53 0,35 0,17 0,39 0,24 0,11 0,23 0,20 0,50 0,46 0,46Optiroc -0,39 -0,33 -0,42 -0,24 -0,32 -0,25 -0,25 -0,13 -0,04 -0,07 -0,08 -0,43 -0,40 -0,42Mica layers 0,29 0,45 0,46 0,78 0,75 0,56 0,73 0,44 0,26 0,39 0,37 0,62 0,59 0,65Average pore size 0,37 0,53 0,61 0,74 0,64 0,42 0,62 0,36 0,18 0,32 0,29 0,60 0,61 0,70Tot. pore area -0,40 -0,47 -0,47 -0,47 -0,38 -0,25 -0,41 -0,24 -0,14 -0,24 -0,21 -0,44 -0,47 -0,49Pore area >900Å -0,19 -0,30 -0,32 -0,41 -0,30 -0,14 -0,31 -0,19 -0,08 -0,18 -0,16 -0,28 -0,29 -0,35Pore area >300Å -0,39 -0,48 -0,47 -0,51 -0,38 -0,22 -0,41 -0,24 -0,12 -0,24 -0,21 -0,44 -0,47 -0,49Pore area 300-900Å -0,46 -0,52 -0,49 -0,49 -0,38 -0,24 -0,41 -0,24 -0,13 -0,24 -0,21 -0,47 -0,51 -0,51Pore area 60-300Å -0,37 -0,40 -0,43 -0,38 -0,33 -0,27 -0,37 -0,22 -0,15 -0,22 -0,20 -0,40 -0,43 -0,44Surface Texture 0,51 0,45 0,42 0,41 0,31 0,18 0,28 0,15 0,04 0,11 0,10 0,48 0,46 0,43LosAngeles 0,42 0,50 0,55 0,74 0,63 0,42 0,60 0,31 0,13 0,26 0,24 0,64 0,60 0,68Share % crushed 0,77 0,78 0,94 0,93 0,70 0,48 0,64 0,34 0,14 0,27 0,25 0,88 0,87 0,95elongation 3.15/4.0 1,00 0,95 0,86 0,77 0,55 0,25 0,51 0,25 0,02 0,19 0,16 0,92 0,92 0,84elongation 1.6/2.0 0,95 1,00 0,84 0,82 0,65 0,29 0,60 0,31 0,06 0,25 0,21 0,94 0,97 0,87elongation 0.8/1.0 0,86 0,84 1,00 0,84 0,65 0,43 0,57 0,30 0,10 0,22 0,21 0,90 0,91 0,96Angularity 0,77 0,82 0,84 1,00 0,76 0,47 0,73 0,39 0,15 0,33 0,30 0,90 0,89 0,93Qnty 3.15/4.0 0,55 0,65 0,65 0,76 1,00 0,87 0,98 0,85 0,68 0,80 0,78 0,77 0,75 0,77Qnty 1.6/2.0 0,25 0,29 0,43 0,47 0,87 1,00 0,88 0,92 0,89 0,89 0,91 0,46 0,44 0,51Qnty 0.8/1.0 0,51 0,60 0,57 0,73 0,98 0,88 1,00 0,89 0,74 0,86 0,84 0,71 0,69 0,71Surface 3.15/4.0 0,25 0,31 0,30 0,39 0,85 0,92 0,89 1,00 0,96 0,99 0,99 0,39 0,38 0,39Surface 1.6/2.0 0,02 0,06 0,10 0,15 0,68 0,89 0,74 0,96 1,00 0,97 0,98 0,14 0,13 0,16Surface 0.8/1.0 0,19 0,25 0,22 0,33 0,80 0,89 0,86 0,99 0,97 1,00 1,00 0,32 0,30 0,31Tot. surface 0,16 0,21 0,21 0,30 0,78 0,91 0,84 0,99 0,98 1,00 1,00 0,29 0,27 0,29flakiness 3.15/4.0 0,92 0,94 0,90 0,90 0,77 0,46 0,71 0,39 0,14 0,32 0,29 1,00 0,99 0,96flakiness 1.6/2.0 0,92 0,97 0,91 0,89 0,75 0,44 0,69 0,38 0,13 0,30 0,27 0,99 1,00 0,95flakiness 0.8/1.0 0,84 0,87 0,96 0,93 0,77 0,51 0,71 0,39 0,16 0,31 0,29 0,96 0,95 1,00

APPENDIX 4 RELIABILITY OF THE SENSISTIVITY ANALYSIS –

FLOW VALUE, AIR %, BLEEDING AND COMPRESSIVE STRENGTHPAGE 1/14

Reliability of sensitivity analysis; SC- flakiness 3,15/4.0 mm-flow value

Reliability of sensitivity analysis; SC- angularity-flow value


0

100

200

300

400

500

600

700

800

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

SC-Angularity

Flo

w v

alu

e [m

m]




Reliability of sensitivity analysis; SC- elongation 3.15/4.0 mm-flow value

Reliability of sensitivity analysis; SC- pore area 300-900Å-flow value


0

100

200

300

400

500

600

700

800

1.34 1.36 1.38 1.4 1.42 1.44 1.46 1.48

SC-Elgn 3.15/4.0 mm

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14


Flo

w v

alu

e [m

m]




Reliability of sensitivity analysis; SC- pore area >900Å-flow value

Reliability of sensitivity analysis; F- mica %-flow value


0

100

200

300

400

500

600

700

800

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

SC-pore area>900Å [m2/g]

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16

F- mica %

Flo

w v

alu

e [m

m]




Reliability of sensitivity analysis; F- Cu-flow value

Reliability of sensitivity analysis; F- BET value-flow value


0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16 18

F - Cu

Flo

w v

alu

e [m

m]



0

100

200

300

400

500

600

700

800

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Flo

w v

alu

e [m

m]




Reliability of sensitivity analysis; F- Zeta potential-flow value

Reliability of sensitivity analysis; SC- pore area 60-300Å-air %


0

100

200

300

400

500

600

700

800

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

F - Zeta pot. [mV]

Flo

w v

alu

e [m

m]



0

1

2

3

4

5

6

7

8

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16


Air

%




Reliability of sensitivity analysis; SC- pore area 300-900Å-air %

Reliability of sensitivity analysis; SC- flakiness 3.15/4.0 mm-air %


0

1

2

3

4

5

6

7

8

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14


Air

%



0

1

2

3

4

5

6

7

8

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Air

%




Reliability of sensitivity analysis; SC- angularity-air %

Reliability of sensitivity analysis; F- Cu-air %


0

1

2

3

4

5

6

7

8

4.5 5 5.5 6 6.5 7 7.5 8 8.5 9

SC-Angularity

Air

%



0

1

2

3

4

5

6

7

8

2 4 6 8 10 12 14 16 18

F - Cu

Air

%




Reliability of sensitivity analysis; F- BET value-air %

Reliability of sensitivity analysis; SC- total pore area-bleeding


0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Air

%



0

2

4

6

8

10

12

14

16

18

20

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4


Ble

edin

g [

g/c

m3]




Reliability of sensitivity analysis; SC- average pore size-bleeding

Reliability of sensitivity analysis; SC- elongation 0.8/1.0 mm-bleeding


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12

SC- avg.pore size [µm]

Ble

edin

g [

g/c

m3]



0

2

4

6

8

10

12

14

16

18

20

1.4 1.45 1.5 1.55 1.6 1.65 1.7

SC-Elgn 0.8/1.0 mm

Ble

edin

g [

g/c

m3]




Reliability of sensitivity analysis; SC- flakiness 1.6/2.0 mm-bleeding

Reliability of sensitivity analysis; SC- elongation 1.6/2.0 mm-bleeding


0

2

4

6

8

10

12

14

16

18

20

1.2 1.25 1.3 1.35 1.4 1.45 1.5

SC-Flkn 1.6/2.0 mm

Ble

edin

g [

g/c

m3]



0

2

4

6

8

10

12

14

16

18

20

1.3 1.35 1.4 1.45 1.5 1.55

SC-Elgn 1.6/2.0 mm

Ble

edin

g [

g/c

m3]




Reliability of sensitivity analysis; F- BET value-bleeding

Reliability of sensitivity analysis; F- Zeta potential-bleeding


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16

F- BET [m2/g]

Ble

edin

g [

g/c

m3]



0

2

4

6

8

10

12

14

16

18

20

-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

F - Zeta pot. [mV]

Ble

edin

g [

g/c

m3]




Reliability of sensitivity analysis; F- density-bleeding

Reliability of sensitivity analysis; F- Cu-bleeding


0

2

4

6

8

10

12

14

16

18

20

2.6 2.65 2.7 2.75 2.8 2.85 2.9 2.95 3 3.05

F- density [Mg/m3]

Ble

edin

g [

g/c

m3]

N30

WR30

AE30

N35WR35

AE35


0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18

F - Cu

Ble

edin

g [

g/c

m3]




Reliability of sensitivity analysis; SC- flakiness 3.15/4.0 mm-compressive strength

Reliability of sensitivity analysis; SC- quantity 1.6/2.0 mm-compressive strength


35

40

45

50

55

60

65

70

1.2 1.25 1.3 1.35 1.4 1.45

SC-Flkn 3.15/4.0 mm

Co

mp

. Ste

ng

th [

MP

a]



35

40

45

50

55

60

65

70

14000000 15000000 16000000 17000000 18000000 19000000 20000000

SC-Qnty 1.6/2.0 mm

Co

mp

. Ste

ng

th [

MP

a]




Reliability of sensitivity analysis; SC- Los Angeles value-compressive strength

Reliability of sensitivity analysis; SC- pore area 60-300Å-compressive strength


35

40

45

50

55

60

65

70

20 22 24 26 28 30 32 34 36 38 40


Co

mp

. Ste

ng

th [

MP

a]


Sensitivity analysis - difference between modelled and measured vcalues

35

40

45

50

55

60

65

70

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16


Co

mp

. Ste

ng

th [

MP

a]


INPUTS OF MODELLED AGGREGATE COMBINATIONS APPENDIX 5

SE

M

AE

WR

BE

T

F -

den

sity

[M

g/m

3]

F -

Zet

a p

ot.

[m

V]

F-

Cu

SC

- av

g. p

ore

siz

e [u

m]

SC

- to

t. p

ore

are

a [m

2/g

]

SC

- L

A v

alu

e (m

od

.) [

%]

SC

- E

lgn

3.1

5/4.

0 m

m

SC

- E

lgn

1.6

/2.0

mm

SC

- E

lgn

0.8

/1.0

mm

SC

- A

ng

ula

rity

SC

- Q

nty

1.6

/2.0

mm

SC

- F

lkn

3.1

5/4.

0 m

m

SC

- F

lkn

1.6

/2.0

mm

F-

Mic

a [%

]

SC

- P

ore

are

a >

900Å

[m

2/g

]

SC

- P

ore

are

a 30

0-90

0Å [

m2/

g]

SC

- P

ore

are

a 60

-300

Å [

m2/

g]

Past gravel 300 0 0 3,4 2,70 -9,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 17148914 1,25 1,25 6,00 0,03 0,04 0,01350 0 0 3,4 2,70 -9,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 15948490 1,25 1,25 6,00 0,03 0,04 0,01300 1 0 3,4 2,70 -9,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 15532017 1,25 1,25 6,00 0,03 0,04 0,01350 1 0 3,4 2,70 -9,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 16634447 1,25 1,25 6,00 0,03 0,04 0,01300 0 1 3,4 2,70 -15,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 17736877 1,25 1,25 6,00 0,03 0,04 0,01350 0 1 3,4 2,70 -15,1 8,8 1,77 0,08 21,5 1,36 1,36 1,46 4,9 16634447 1,25 1,25 6,00 0,03 0,04 0,01

Future gravel 300 0 0 9,3 2,71 -7,1 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 16991626 1,23 1,23 0,00 0,10 0,06 0,02350 0 0 9,3 2,71 -7,1 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 15802213 1,23 1,23 0,00 0,10 0,06 0,02300 1 0 9,3 2,71 -7,1 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 15389559 1,23 1,23 0,00 0,10 0,06 0,02350 1 0 9,3 2,71 -7,1 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 16481878 1,23 1,23 0,00 0,10 0,06 0,02300 0 1 9,3 2,71 -15,2 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 17574196 1,23 1,23 0,00 0,10 0,06 0,02350 0 1 9,3 2,71 -15,2 16,2 1,00 0,18 24,1 1,35 1,35 1,46 5,2 16481878 1,23 1,23 0,00 0,10 0,06 0,02

Good strenght&good shape 300 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 17316121 1,33 1,34 6,00 0,02 0,00 0,00350 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 16103993 1,33 1,34 6,00 0,02 0,00 0,00

Filler aggregate and 300 1 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 15683459 1,33 1,34 6,00 0,02 0,00 0,00crushed rock 350 1 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 16796638 1,33 1,34 6,00 0,02 0,00 0,00

300 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 17909817 1,33 1,34 6,00 0,02 0,00 0,00350 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 19,6 1,41 1,46 1,59 7,1 16796638 1,33 1,34 6,00 0,02 0,00 0,00

Good strength&poor shape 300 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,43 1,51 1,57 8,6 18488322 1,43 1,42 6,00 0,02 0,00 0,00350 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 19,6 1,43 1,51 1,57 8,6 17194139 1,43 1,42 6,00 0,02 0,00 0,00


300 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 19,6 1,43 1,51 1,57 8,6 19122207 1,43 1,42 6,00 0,02 0,00 0,00350 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 19,6 1,43 1,51 1,57 8,6 17933672 1,43 1,42 6,00 0,02 0,00 0,00

Poor strength&poor shape 300 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 38,2 1,43 1,51 1,57 8,6 18488322 1,43 1,42 6,00 0,02 0,00 0,00350 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 38,2 1,43 1,51 1,57 8,6 17194139 1,43 1,42 6,00 0,02 0,00 0,00


300 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 38,2 1,43 1,51 1,57 8,6 19122207 1,43 1,42 6,00 0,02 0,00 0,00350 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 38,2 1,43 1,51 1,57 8,6 17933672 1,43 1,42 6,00 0,02 0,00 0,00

Poor strength&good shape 300 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 38,2 1,41 1,46 1,59 7,1 17316121 1,33 1,34 6,00 0,02 0,00 0,00350 0 0 3,1 2,72 -8,8 10,7 7,62 0,02 38,2 1,41 1,46 1,59 7,1 16103993 1,33 1,34 6,00 0,02 0,00 0,00


300 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 38,2 1,41 1,46 1,59 7,1 17909817 1,33 1,34 6,00 0,02 0,00 0,00350 0 1 3,1 2,72 -13,7 10,7 7,62 0,02 38,2 1,41 1,46 1,59 7,1 16796638 1,33 1,34 6,00 0,02 0,00 0,00

N30 FLOW VALUE_COMBINED EFFECT APPENDIX 6A1/6

Testing data 300 0 0 3,12 2,718 -8,8 10,7 7,62 0,019 19,6 1,41 1,46 1,59 7,1 17316121 1,33 1,34 6 0,019 0 0stdev min max average

SC-Flkn 3.15/4.0 mm and SC-Pore area 300-900Å -> Flow value 49,6 304 470 364Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,43

0,00 470 450 430 410 392 376 362 351 342 3370,01 470 448 425 403 383 365 350 339 331 3270,03 469 444 419 395 373 354 339 328 321 3180,04 466 439 412 386 363 344 329 319 313 3120,06 461 432 404 378 355 336 321 312 307 3070,07 454 425 396 370 347 329 316 307 304 3060,09 446 417 389 364 342 324 312 306 304 3070,10 438 410 383 359 338 322 312 307 307 3110,12 430 403 378 356 337 324 315 311 313 3180,13 423 398 376 355 339 327 321 319 321 327

41,1 39 218 103 6,9 5 33 15Difference between the 10% and 90% quantiles [MPa] Coefficient of variation for the expectation value [%]

81 68 59 50 42 39 40 46 55 68 8,6 7,6 6,8 6,1 5,4 5,2 5,5 6,5 8,1 10,087 73 62 53 46 42 43 48 56 68 9,3 8,2 7,3 6,6 6,0 5,8 6,1 7,1 8,5 10,496 81 69 60 56 54 55 60 68 80 10,2 9,1 8,2 7,6 7,5 7,6 8,2 9,1 10,6 12,5

105 89 77 70 66 66 70 76 85 98 11,2 10,1 9,4 9,1 9,1 9,6 10,6 11,9 13,6 15,7112 98 87 81 80 82 87 94 104 117 12,2 11,3 10,8 10,8 11,2 12,2 13,6 15,0 16,9 19,0121 106 97 93 93 97 103 113 126 139 13,4 12,5 12,3 12,5 13,4 14,8 16,4 18,4 20,6 22,8129 115 106 102 105 114 123 134 146 160 14,4 13,7 13,6 14,0 15,3 17,5 19,7 21,9 24,0 26,1136 123 114 113 119 126 137 151 165 182 15,5 14,9 14,9 15,8 17,6 19,5 22,0 24,6 26,8 29,2139 127 121 123 129 140 154 169 184 201 16,2 15,8 16,0 17,2 19,2 21,7 24,4 27,1 29,5 31,6146 134 130 133 142 155 170 185 201 218 17,3 16,8 17,3 18,7 20,9 23,6 26,4 29,1 31,3 33,3

stdev min max averageSC- Flkn 3.15/4.0 mm and F- Cu -> Flow value 45,4 332 489 394

Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 489 468 448 427 407 390 374 362 352 3465 485 465 444 424 404 387 372 359 350 3446 482 461 441 421 401 384 369 357 348 3428 478 458 437 417 398 381 367 355 346 3409 474 454 434 414 395 379 364 353 344 339

11 470 450 430 410 392 376 362 351 342 33712 466 446 426 407 389 373 360 349 341 33614 462 442 423 404 386 371 357 347 339 33415 457 438 419 400 383 368 355 345 337 33316 453 434 415 397 380 365 353 343 336 332


89 77 67 59 53 50 51 55 63 73 9,1 8,2 7,5 6,9 6,5 6,5 6,8 7,6 8,9 10,587 74 63 56 49 46 47 51 59 69 9,0 8,0 7,1 6,6 6,1 6,0 6,3 7,1 8,4 10,186 73 62 53 47 44 44 48 57 68 8,9 7,9 7,0 6,3 5,8 5,7 5,9 6,8 8,1 9,985 72 61 52 45 41 41 46 55 67 8,9 7,8 6,9 6,2 5,6 5,4 5,6 6,5 8,0 9,883 70 59 50 43 40 40 46 54 66 8,7 7,7 6,8 6,1 5,5 5,3 5,5 6,5 7,9 9,782 69 58 49 42 38 39 45 54 67 8,7 7,7 6,7 5,9 5,3 5,1 5,4 6,4 7,9 10,081 67 57 48 41 38 40 46 55 68 8,7 7,5 6,6 5,9 5,3 5,1 5,6 6,6 8,1 10,180 67 57 48 42 39 41 47 57 70 8,7 7,6 6,8 6,0 5,4 5,3 5,7 6,8 8,4 10,480 67 57 48 43 41 43 50 60 73 8,8 7,7 6,8 6,0 5,6 5,6 6,1 7,2 8,9 10,981 69 58 50 45 43 45 52 62 76 9,0 7,9 7,0 6,3 5,9 5,8 6,4 7,6 9,3 11,5

N35 FLOW VALUE_COMBINED EFFECT APPENDIX 6A2/6



0,00 547 539 530 521 512 504 497 491 486 4830,01 555 544 532 519 508 497 488 481 476 4730,03 561 547 532 517 502 489 479 471 465 4620,04 566 550 532 514 497 482 469 460 454 4510,06 569 550 530 510 491 474 461 451 444 4410,07 570 550 528 506 485 467 453 442 436 4330,09 569 548 525 502 481 462 447 436 430 4270,10 566 545 522 499 477 459 443 432 426 4230,12 562 541 519 496 475 457 442 431 424 4210,13 556 536 515 494 474 456 442 432 425 422


70 58 47 39 35 35 38 45 54 66 6,4 5,3 4,4 3,8 3,4 3,5 3,8 4,5 5,5 6,976 62 52 45 41 39 41 46 55 66 6,8 5,7 4,9 4,4 4,0 3,9 4,2 4,8 5,8 7,084 71 60 54 51 50 52 58 65 76 7,5 6,5 5,7 5,2 5,1 5,1 5,5 6,1 7,0 8,295 80 71 66 63 64 67 72 81 91 8,3 7,3 6,7 6,4 6,4 6,6 7,1 7,9 8,9 10,1

104 91 82 77 76 79 83 90 99 109 9,1 8,3 7,7 7,6 7,8 8,3 9,1 10,0 11,2 12,3113 100 91 87 88 92 98 107 117 132 9,9 9,1 8,7 8,6 9,0 9,8 10,8 12,1 13,4 15,2123 109 101 98 101 107 115 125 135 148 10,8 10,0 9,6 9,8 10,5 11,6 12,9 14,4 15,7 17,4128 115 107 106 110 118 128 141 154 169 11,3 10,5 10,3 10,6 11,6 12,9 14,5 16,3 18,1 20,0134 121 116 117 122 132 144 157 169 185 12,0 11,2 11,1 11,8 12,9 14,5 16,3 18,2 19,9 21,9138 127 122 125 132 145 159 174 190 206 12,4 11,8 11,8 12,6 14,0 15,9 18,0 20,2 22,4 24,4


Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 569 562 553 545 535 527 519 513 508 5035 565 558 549 540 531 523 515 509 504 5006 561 553 545 536 527 518 511 505 500 4968 556 549 540 531 522 514 506 500 495 4929 552 544 535 526 517 509 501 496 491 488

11 547 539 530 521 512 504 497 491 486 48312 542 534 525 516 507 499 492 486 482 47914 538 529 520 511 502 493 487 481 477 47415 533 524 515 505 496 488 481 476 472 47016 528 519 510 500 491 483 476 471 467 465


77 64 55 50 47 46 47 52 58 68 6,8 5,7 5,0 4,6 4,4 4,3 4,6 5,0 5,7 6,774 62 53 47 43 43 45 49 56 67 6,5 5,6 4,8 4,3 4,1 4,1 4,4 4,8 5,6 6,773 61 51 45 41 41 43 48 56 66 6,5 5,5 4,7 4,2 3,9 3,9 4,2 4,8 5,6 6,772 60 50 42 39 38 41 47 55 66 6,5 5,5 4,6 4,0 3,7 3,7 4,0 4,7 5,6 6,770 58 49 41 37 37 40 46 55 66 6,3 5,3 4,5 3,9 3,6 3,6 3,9 4,6 5,6 6,870 57 47 40 35 35 38 45 54 65 6,4 5,3 4,5 3,8 3,4 3,5 3,9 4,6 5,5 6,870 58 47 39 35 35 38 44 53 66 6,5 5,4 4,5 3,8 3,4 3,5 3,8 4,5 5,5 6,970 57 47 39 35 34 38 44 55 67 6,5 5,4 4,5 3,8 3,4 3,4 3,9 4,6 5,7 7,171 57 47 40 36 35 39 46 55 68 6,7 5,5 4,6 3,9 3,6 3,6 4,0 4,8 5,9 7,372 58 47 40 36 37 41 48 58 73 6,8 5,6 4,6 4,0 3,7 3,8 4,3 5,1 6,2 7,8

AE30 FLOW VALUE_COMBINED EFFECT APPENDIX 6A3/6



0,00 427 406 385 366 348 333 321 312 307 3050,01 425 401 378 356 336 320 308 299 295 2940,03 422 396 370 346 325 309 296 288 284 2850,04 418 389 362 337 315 298 286 279 276 2780,06 412 382 354 328 307 290 278 272 271 2750,07 405 375 347 321 300 284 273 268 269 2740,09 398 368 340 315 295 280 271 268 270 2770,10 391 362 336 312 293 280 273 271 275 2830,12 385 358 333 312 295 284 278 278 283 2920,13 380 355 333 314 299 290 286 288 294 303


82 69 58 48 42 38 40 47 57 71 9,6 8,5 7,5 6,6 6,0 5,8 6,2 7,5 9,3 11,688 73 61 52 44 40 41 47 57 71 10,3 9,1 8,1 7,2 6,6 6,2 6,7 7,8 9,7 12,296 81 68 58 51 49 52 59 69 84 11,4 10,2 9,2 8,4 7,9 8,0 8,8 10,3 12,2 14,8

105 89 77 68 63 63 67 76 87 102 12,6 11,5 10,7 10,0 10,0 10,5 11,7 13,6 15,7 18,3113 97 85 77 75 79 86 93 105 122 13,7 12,7 12,0 11,8 12,2 13,7 15,4 17,1 19,4 22,3120 104 93 88 88 93 102 114 127 143 14,8 13,9 13,5 13,7 14,8 16,5 18,7 21,2 23,6 26,1127 111 101 99 101 108 119 133 149 167 15,9 15,1 14,9 15,7 17,0 19,3 22,0 24,9 27,6 30,1134 119 110 109 115 124 135 150 167 185 17,1 16,4 16,4 17,4 19,5 22,1 24,7 27,7 30,4 32,7138 123 117 118 126 138 152 170 188 207 17,9 17,2 17,5 19,0 21,3 24,3 27,4 30,5 33,2 35,5143 131 126 130 140 154 170 187 207 230 18,8 18,4 19,0 20,7 23,4 26,6 29,7 32,5 35,3 37,9


Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 449 427 405 384 364 347 333 323 316 3135 444 423 401 380 361 344 331 320 314 3116 440 419 397 376 358 341 328 318 312 3098 436 414 393 373 354 338 326 316 310 3089 431 410 389 369 351 336 323 314 308 306

11 427 406 385 366 348 333 321 312 307 30512 423 402 381 362 345 330 319 310 305 30414 418 398 377 359 342 328 317 309 304 30315 414 393 374 355 339 326 315 307 303 30216 410 389 370 352 337 324 313 306 302 302


92 81 70 60 55 53 54 58 66 77 10,2 9,5 8,6 7,8 7,5 7,6 8,1 9,0 10,4 12,291 77 66 57 51 49 50 54 62 73 10,2 9,1 8,2 7,5 7,1 7,1 7,5 8,4 9,9 11,788 76 64 55 48 45 45 50 59 70 10,0 9,0 8,1 7,3 6,7 6,5 6,9 7,9 9,4 11,486 73 61 52 45 42 43 48 57 70 9,9 8,8 7,8 7,0 6,4 6,2 6,6 7,6 9,2 11,485 71 59 49 43 40 41 47 56 69 9,9 8,7 7,5 6,7 6,2 5,9 6,4 7,4 9,2 11,382 68 57 48 41 38 40 46 57 71 9,6 8,4 7,4 6,6 5,9 5,7 6,2 7,4 9,4 11,781 67 56 47 41 37 40 47 58 71 9,6 8,4 7,4 6,5 5,9 5,7 6,2 7,6 9,5 11,780 66 55 46 40 38 41 49 60 74 9,6 8,4 7,3 6,4 5,9 5,8 6,5 7,9 9,8 12,380 67 55 46 40 40 43 51 63 77 9,7 8,5 7,4 6,5 6,0 6,1 6,8 8,3 10,4 12,881 67 56 48 43 41 46 54 66 80 9,9 8,7 7,6 6,8 6,3 6,4 7,3 8,8 10,8 13,3

AE35 FLOW VALUE_COMBINED EFFECT APPENDIX 6A4/6



0,00 493 483 474 464 454 446 439 434 431 4290,01 497 485 472 459 447 437 429 423 419 4190,03 500 485 469 454 440 428 418 412 408 4080,04 502 485 467 449 433 419 409 402 398 3980,06 503 483 463 444 426 411 400 393 389 3900,07 502 481 460 439 420 405 393 386 383 3830,09 500 479 457 435 416 400 389 381 379 3800,10 497 476 454 432 414 398 387 380 377 3790,12 494 473 451 431 413 398 387 381 379 3800,13 490 470 450 431 414 400 390 385 383 384


72 60 50 43 38 37 38 44 52 65 7,3 6,2 5,3 4,6 4,2 4,1 4,4 5,1 6,1 7,578 65 55 48 43 40 41 46 54 65 7,9 6,7 5,8 5,2 4,8 4,5 4,8 5,4 6,4 7,886 73 64 57 52 50 51 56 63 75 8,6 7,6 6,8 6,3 5,9 5,9 6,1 6,8 7,8 9,297 84 74 67 64 64 66 71 79 91 9,6 8,6 7,9 7,5 7,4 7,6 8,1 8,8 9,9 11,4

106 93 84 78 76 77 81 89 98 110 10,5 9,6 9,1 8,8 8,9 9,4 10,2 11,4 12,6 14,1114 101 93 89 89 92 98 107 117 130 11,3 10,5 10,1 10,1 10,6 11,4 12,5 13,9 15,2 16,9122 110 102 99 101 106 115 125 137 152 12,2 11,5 11,2 11,4 12,1 13,3 14,8 16,4 18,1 20,0129 117 109 108 113 121 132 143 157 172 13,0 12,3 12,1 12,5 13,7 15,2 17,0 18,8 20,8 22,7133 123 117 118 123 133 144 158 174 191 13,5 13,0 13,0 13,7 14,9 16,7 18,6 20,7 23,0 25,1136 126 124 128 136 147 160 176 192 210 13,9 13,4 13,7 14,8 16,5 18,3 20,5 22,9 25,1 27,3


Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 516 507 497 487 477 469 461 455 451 4485 511 502 493 483 473 464 457 451 447 4456 507 498 488 478 468 460 452 447 443 4418 502 493 483 473 464 455 448 442 439 4379 498 488 478 468 459 450 443 438 435 433

11 493 484 474 464 454 446 439 434 431 42912 488 479 469 459 449 441 434 429 426 42514 483 474 464 454 445 437 430 425 422 42215 479 469 459 449 440 432 426 421 418 41816 474 465 455 445 436 428 421 417 415 414


80 69 60 53 49 48 48 51 56 66 7,8 6,8 6,0 5,5 5,2 5,1 5,2 5,6 6,3 7,478 66 57 51 46 45 46 49 56 65 7,7 6,6 5,8 5,2 4,9 4,8 5,0 5,4 6,3 7,475 63 54 47 43 42 43 48 55 65 7,4 6,4 5,5 5,0 4,6 4,6 4,8 5,4 6,2 7,473 61 53 46 41 40 41 45 53 64 7,3 6,2 5,4 4,8 4,4 4,3 4,6 5,1 6,1 7,373 61 51 43 39 38 40 45 53 65 7,3 6,3 5,4 4,6 4,2 4,2 4,5 5,1 6,1 7,472 60 50 42 38 36 38 44 53 64 7,3 6,2 5,3 4,6 4,1 4,1 4,4 5,1 6,1 7,571 59 49 42 37 35 38 44 53 65 7,3 6,2 5,3 4,6 4,1 4,0 4,4 5,1 6,2 7,672 59 49 41 36 35 38 44 53 66 7,4 6,2 5,3 4,5 4,1 4,0 4,4 5,2 6,3 7,872 59 49 41 37 36 39 45 55 67 7,5 6,3 5,3 4,6 4,2 4,2 4,6 5,4 6,5 8,172 59 49 42 38 37 41 48 58 70 7,6 6,4 5,4 4,7 4,3 4,4 4,9 5,7 6,9 8,5

WR30 FLOW VALUE_COMBINED EFFECT APPENDIX 6A5/6



0,00 535 511 485 458 431 407 384 366 351 3410,01 530 503 474 446 418 392 370 352 338 3290,03 521 492 462 432 403 378 356 339 326 3190,04 510 480 449 418 389 364 343 328 317 3120,06 497 466 435 404 376 352 333 319 310 3070,07 482 452 421 391 365 343 325 313 307 3060,09 468 438 408 381 356 336 321 311 307 3080,10 454 426 398 372 350 332 320 312 310 3130,12 442 416 390 367 348 332 322 317 317 3210,13 431 408 385 365 348 336 328 325 326 331


83 69 58 48 42 40 42 49 58 70 7,7 6,8 5,9 5,2 4,8 4,9 5,5 6,7 8,3 10,389 75 63 54 47 44 46 52 60 72 8,4 7,5 6,7 6,0 5,6 5,6 6,2 7,3 8,9 10,998 83 72 64 59 57 59 65 73 84 9,5 8,5 7,8 7,4 7,3 7,6 8,3 9,5 11,2 13,2

110 96 83 76 73 73 76 83 92 103 10,8 10,0 9,3 9,1 9,3 10,0 11,1 12,7 14,5 16,6118 104 94 88 87 89 96 104 111 123 11,8 11,1 10,8 10,9 11,5 12,7 14,5 16,3 17,9 20,1127 114 105 100 102 106 113 124 135 149 13,2 12,6 12,5 12,8 14,0 15,5 17,4 19,8 22,1 24,3134 121 113 112 115 121 131 143 156 170 14,3 13,8 13,8 14,7 16,1 18,1 20,5 23,0 25,4 27,6139 129 122 122 128 138 150 163 176 192 15,4 15,1 15,3 16,4 18,3 20,8 23,4 26,1 28,3 30,7145 134 129 132 141 152 165 181 197 215 16,5 16,1 16,6 18,0 20,2 22,9 25,6 28,5 31,1 33,6148 137 135 141 152 166 182 198 216 233 17,2 16,8 17,5 19,2 21,7 24,7 27,7 30,5 33,1 35,2


Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 551 528 503 476 450 424 401 382 365 3545 549 525 500 473 446 421 398 378 363 3516 546 522 496 470 443 418 395 375 360 3488 543 519 493 466 439 414 391 372 357 3469 539 515 489 462 435 410 388 369 354 343

11 535 511 485 458 431 407 384 366 351 34112 531 507 481 454 427 403 381 362 348 33814 527 502 476 449 423 399 377 359 345 33615 522 498 471 445 419 395 373 356 342 33416 517 493 467 440 414 390 370 353 340 331


89 77 68 60 54 52 52 55 61 70 8,1 7,3 6,8 6,3 6,0 6,1 6,4 7,2 8,4 9,988 75 65 57 51 47 48 51 58 67 8,0 7,2 6,5 6,0 5,7 5,6 6,0 6,8 8,0 9,688 75 64 54 48 44 45 50 57 67 8,0 7,2 6,4 5,8 5,4 5,3 5,7 6,6 8,0 9,686 73 61 52 46 42 43 48 56 67 7,9 7,0 6,2 5,6 5,2 5,1 5,5 6,5 7,9 9,784 71 59 50 43 41 43 49 57 68 7,8 6,9 6,1 5,4 5,0 5,0 5,5 6,7 8,1 9,984 70 57 48 42 40 42 48 58 70 7,8 6,8 5,9 5,2 4,9 4,9 5,5 6,6 8,2 10,383 68 56 47 41 39 42 49 59 71 7,8 6,7 5,9 5,1 4,8 4,8 5,5 6,8 8,5 10,581 67 55 46 40 39 44 51 62 74 7,7 6,6 5,8 5,1 4,7 4,9 5,8 7,1 8,9 11,181 67 55 46 42 41 47 55 65 77 7,8 6,7 5,9 5,2 5,0 5,2 6,2 7,7 9,5 11,682 68 56 48 44 44 50 59 70 82 7,9 6,9 6,0 5,5 5,3 5,7 6,8 8,3 10,2 12,4

WR35 FLOW VALUE_COMBINED EFFECT APPENDIX 6A6/6



0,00 699 696 689 681 671 660 649 638 627 6150,01 700 693 683 671 659 646 634 622 610 5990,03 695 685 672 658 643 628 615 602 590 5800,04 686 673 657 641 624 607 593 580 568 5590,06 673 658 640 621 602 585 570 557 546 5370,07 657 640 621 600 581 563 547 534 524 5160,09 640 621 601 580 560 541 526 513 504 4970,10 621 602 581 560 540 522 507 496 487 4810,12 601 583 563 543 523 506 492 481 473 4680,13 582 565 546 527 509 493 480 470 463 459


81 68 57 48 42 39 40 47 57 70 5,8 4,9 4,1 3,5 3,1 2,9 3,1 3,7 4,6 5,785 72 62 54 47 44 45 50 58 70 6,1 5,2 4,6 4,0 3,6 3,4 3,5 4,0 4,8 5,893 82 72 64 60 58 59 63 70 79 6,7 6,0 5,3 4,9 4,7 4,6 4,8 5,2 5,9 6,8

102 91 83 76 73 73 76 80 87 96 7,4 6,8 6,3 5,9 5,9 6,0 6,4 6,9 7,6 8,6111 100 93 88 87 89 93 100 108 118 8,3 7,6 7,2 7,1 7,2 7,6 8,2 9,0 9,9 11,0120 109 103 100 102 106 112 120 129 138 9,1 8,5 8,3 8,4 8,8 9,4 10,2 11,2 12,3 13,4128 119 113 111 114 121 129 139 149 161 10,0 9,6 9,4 9,6 10,2 11,1 12,3 13,5 14,8 16,2136 126 120 121 124 133 142 156 168 180 10,9 10,5 10,3 10,8 11,5 12,7 14,0 15,7 17,3 18,7140 128 125 127 135 146 159 173 186 200 11,6 11,0 11,1 11,7 12,9 14,4 16,2 18,0 19,6 21,4144 135 134 137 146 159 172 188 203 216 12,4 12,0 12,2 13,0 14,4 16,1 17,9 20,0 21,9 23,6


Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,433 710 709 706 700 693 684 675 664 653 6405 709 707 703 697 690 681 671 660 649 6366 707 705 701 694 686 677 667 656 644 6328 705 703 698 690 682 672 661 650 639 6279 703 699 694 686 677 666 656 644 633 621

11 699 696 689 681 671 660 649 638 627 61512 696 691 684 675 665 654 643 631 620 60814 691 686 679 669 658 647 635 624 612 60115 687 681 673 662 651 639 628 616 605 59416 682 675 666 655 644 632 620 608 597 586


87 75 65 56 50 47 47 50 56 65 6,1 5,3 4,6 4,0 3,6 3,4 3,5 3,7 4,3 5,185 72 62 54 48 45 45 49 56 65 6,0 5,1 4,4 3,9 3,5 3,3 3,3 3,7 4,3 5,184 71 61 52 45 43 44 49 56 66 5,9 5,0 4,3 3,7 3,3 3,2 3,3 3,7 4,3 5,382 69 59 50 44 42 43 48 56 67 5,8 4,9 4,2 3,7 3,3 3,1 3,2 3,7 4,4 5,482 69 58 49 43 40 41 47 56 68 5,8 4,9 4,2 3,6 3,2 3,0 3,2 3,6 4,4 5,581 68 57 48 41 38 40 47 57 70 5,8 4,9 4,2 3,5 3,1 2,9 3,1 3,7 4,6 5,780 68 57 48 41 38 40 48 59 72 5,8 4,9 4,2 3,5 3,1 2,9 3,1 3,8 4,7 6,081 68 57 49 42 40 43 51 62 75 5,8 5,0 4,2 3,6 3,2 3,1 3,3 4,1 5,0 6,283 70 59 51 45 43 47 56 66 79 6,0 5,1 4,4 3,8 3,4 3,4 3,8 4,5 5,4 6,785 72 62 54 50 49 53 62 74 87 6,2 5,3 4,7 4,1 3,9 3,9 4,3 5,1 6,2 7,4

AE30 AIR % _ COMBINED EFFECT APPENDIX 6B1/2


SC- Flkn 3.15/4.0 mm and SC- Pore area 60-300Å -> Air % 1,0 2 5 3Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,43

0,00 5,1 4,9 4,7 4,6 4,4 4,3 4,2 4,1 4,1 4,00,02 4,9 4,6 4,4 4,2 4,1 3,9 3,8 3,7 3,7 3,60,03 3,9 3,7 3,6 3,4 3,3 3,2 3,1 3,0 3,0 3,00,05 2,9 2,8 2,7 2,6 2,5 2,4 2,4 2,3 2,3 2,30,06 2,3 2,2 2,1 2,0 2,0 2,0 1,9 1,9 1,9 1,90,08 2,0 1,9 1,9 1,8 1,8 1,8 1,8 1,8 1,8 1,80,10 1,9 1,9 1,9 1,8 1,8 1,8 1,8 1,8 1,8 1,80,11 2,1 2,0 2,0 2,0 2,0 1,9 1,9 1,9 1,9 1,90,13 2,2 2,2 2,2 2,2 2,1 2,1 2,1 2,0 2,0 2,00,14 2,3 2,3 2,3 2,3 2,3 2,2 2,2 2,1 2,1 2,1


2,9 2,4 2,0 1,7 1,4 1,2 1,1 1,1 1,2 1,4 27,9 24,7 21,2 18,4 15,8 13,7 12,8 13,0 14,4 16,92,8 2,5 2,2 2,0 1,9 1,9 2,0 2,1 2,3 2,5 29,1 26,5 24,5 23,6 23,9 24,5 26,1 28,6 31,2 33,93,0 2,7 2,4 2,2 2,1 2,0 2,1 2,2 2,3 2,5 39,0 36,2 33,8 32,3 31,6 32,0 33,4 35,8 38,9 42,02,9 2,7 2,5 2,3 2,2 2,2 2,2 2,3 2,4 2,6 50,3 48,7 47,0 45,2 44,9 45,5 46,8 48,9 51,7 54,92,9 2,8 2,7 2,6 2,6 2,6 2,6 2,7 2,8 2,9 64,8 64,1 63,8 64,5 65,5 66,3 68,2 69,8 72,1 74,63,3 3,2 3,2 3,1 3,1 3,1 3,1 3,2 3,3 3,3 83,6 83,7 84,3 85,5 85,7 87,4 89,2 91,7 93,3 94,43,8 3,8 3,7 3,7 3,6 3,7 3,6 3,7 3,8 3,8 97,1 99,2 99,9 100,9 100,4 101,8 102,3 103,3 106,5 107,24,2 4,2 4,2 4,0 4,0 4,0 4,0 4,0 4,1 4,1 101,8 102,2 102,8 101,5 102,6 104,1 105,5 106,9 110,1 111,04,4 4,3 4,3 4,3 4,3 4,3 4,3 4,5 4,5 4,3 97,6 97,7 98,3 99,5 100,9 102,9 104,6 109,2 110,9 108,64,4 4,4 4,4 4,4 4,4 4,4 4,5 4,5 4,5 4,4 93,2 93,3 95,5 96,6 97,2 99,1 101,9 104,2 106,3 107,1

stdev min max averageF- Cu and SC- Pore area 60-300Å -> Air % 0,9 2 5 3

Surface 3,39 4,85 6,30 7,76 9,21 10,67 12,12 13,58 15,03 16,480,00 4,7 4,7 4,6 4,5 4,4 4,4 4,3 4,2 4,1 4,00,02 4,5 4,4 4,3 4,2 4,1 4,0 3,9 3,8 3,7 3,70,03 3,6 3,5 3,5 3,4 3,3 3,2 3,1 3,1 3,0 2,90,05 2,8 2,7 2,6 2,6 2,5 2,5 2,4 2,3 2,3 2,30,06 2,2 2,1 2,1 2,1 2,0 2,0 1,9 1,9 1,9 1,80,08 1,9 1,9 1,9 1,8 1,8 1,8 1,8 1,7 1,7 1,70,10 1,9 1,9 1,9 1,9 1,8 1,8 1,8 1,8 1,7 1,70,11 2,1 2,1 2,0 2,0 2,0 2,0 1,9 1,9 1,9 1,90,13 2,3 2,2 2,2 2,2 2,2 2,1 2,1 2,1 2,1 2,00,14 2,4 2,4 2,3 2,3 2,3 2,3 2,2 2,2 2,2 2,1


1,7 1,6 1,5 1,4 1,3 1,3 1,3 1,2 1,2 1,3 17,5 16,8 16,2 15,5 15,1 14,9 14,8 14,8 15,1 15,62,5 2,4 2,3 2,1 2,0 1,9 1,9 1,8 1,8 1,8 28,5 27,3 26,3 25,5 24,6 24,0 23,7 23,6 23,7 24,32,6 2,5 2,3 2,2 2,1 2,0 2,0 1,9 1,9 1,9 36,5 35,2 33,1 32,2 32,2 31,8 31,3 31,4 31,8 32,82,6 2,5 2,4 2,3 2,2 2,2 2,1 2,1 2,1 2,1 46,5 45,6 45,1 44,6 44,5 44,5 44,8 45,2 46,2 46,92,8 2,8 2,7 2,6 2,6 2,6 2,6 2,6 2,6 2,6 64,3 64,4 64,7 63,7 64,4 65,7 66,6 67,9 68,9 70,13,2 3,2 3,2 3,1 3,1 3,1 3,1 3,0 3,1 3,1 83,3 83,9 84,4 85,2 85,1 86,3 87,5 87,9 89,2 92,73,8 3,7 3,7 3,7 3,7 3,7 3,6 3,6 3,6 3,6 97,9 97,3 98,2 98,9 100,3 101,1 101,6 102,6 103,8 104,74,2 4,2 4,2 4,2 4,2 4,1 4,0 4,0 4,1 4,0 101,2 102,0 102,3 105,7 106,1 103,5 103,9 104,5 107,6 108,34,5 4,5 4,4 4,4 4,3 4,3 4,3 4,2 4,2 4,2 100,9 100,9 99,9 100,0 100,4 100,7 101,0 101,9 102,3 103,24,7 4,6 4,6 4,6 4,4 4,4 4,4 4,4 4,3 4,2 98,5 98,3 98,9 98,8 97,3 97,4 99,4 99,6 99,7 98,9

AE35 AIR % _ COMBINED EFFECT APPENDIX 6B2/2


SC- Flkn 3.15/4.0 mm and SC- Pore area 60-300Å -> Air % 1,2 2 7 3Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,43

0,00 4,5 4,8 5,0 5,3 5,6 5,9 6,2 6,4 6,6 6,60,02 4,2 4,4 4,6 4,8 5,1 5,4 5,6 5,8 6,0 6,00,03 3,5 3,7 3,8 4,0 4,1 4,3 4,5 4,6 4,7 4,80,05 2,9 3,0 3,1 3,1 3,2 3,3 3,4 3,5 3,6 3,60,06 2,5 2,5 2,6 2,6 2,7 2,7 2,8 2,8 2,8 2,80,08 2,4 2,4 2,4 2,4 2,4 2,4 2,4 2,4 2,4 2,40,10 2,5 2,5 2,5 2,4 2,4 2,4 2,4 2,4 2,3 2,30,11 2,7 2,7 2,7 2,6 2,6 2,5 2,5 2,4 2,4 2,30,13 2,9 2,9 2,9 2,8 2,8 2,7 2,6 2,5 2,4 2,30,14 3,0 3,0 3,0 2,9 2,8 2,8 2,7 2,6 2,5 2,4


2,3 2,1 1,8 1,6 1,5 1,4 1,4 1,5 1,6 1,9 25,8 21,7 17,8 14,9 13,2 12,0 11,3 11,4 12,3 14,52,4 2,2 2,1 2,0 2,0 2,1 2,3 2,6 2,9 3,2 28,3 25,4 22,6 20,4 19,4 19,5 20,4 22,0 24,0 26,72,9 2,7 2,5 2,4 2,3 2,4 2,6 2,8 3,1 3,5 41,2 37,1 33,4 29,9 27,9 27,8 28,5 30,3 33,2 36,63,4 3,3 3,2 3,1 3,0 3,0 3,1 3,3 3,5 3,8 58,3 54,7 51,6 48,7 46,1 44,9 45,1 46,4 48,7 51,83,7 3,7 3,7 3,6 3,6 3,6 3,7 3,9 3,9 4,1 74,1 72,6 70,7 69,0 66,6 66,2 67,6 69,0 69,4 71,73,9 4,0 3,9 3,9 3,9 4,0 4,1 4,1 4,2 4,2 82,8 82,8 81,8 79,7 79,8 82,4 83,1 84,4 85,0 87,14,5 4,5 4,5 4,5 4,5 4,4 4,4 4,4 4,4 4,5 91,9 91,0 91,6 91,8 92,2 90,6 91,8 92,5 94,1 98,85,1 5,1 5,0 4,9 5,0 4,9 4,8 4,8 4,8 4,7 95,5 95,0 93,8 94,1 96,1 97,5 97,5 99,3 101,4 102,75,4 5,5 5,4 5,5 5,5 5,4 5,3 5,2 5,1 5,0 94,1 94,7 94,8 97,9 99,5 101,4 101,6 104,2 104,9 107,45,7 5,7 5,7 5,7 5,7 5,6 5,5 5,4 5,4 5,3 96,2 96,0 96,5 98,4 100,1 101,6 104,1 106,1 108,8 112,0

stdev min max averageF- Cu and SC- Pore area 60-300Å -> Air % 1,2 2 6 3

Surface 3,39 4,85 6,30 7,76 9,21 10,67 12,12 13,58 15,03 16,480,00 5,9 5,9 5,8 5,8 5,7 5,7 5,6 5,6 5,5 5,40,02 5,5 5,4 5,4 5,3 5,3 5,2 5,1 5,1 5,0 4,90,03 4,5 4,4 4,4 4,3 4,3 4,2 4,1 4,1 4,0 4,00,05 3,5 3,5 3,4 3,4 3,3 3,3 3,2 3,2 3,1 3,10,06 2,9 2,8 2,8 2,8 2,7 2,7 2,7 2,6 2,6 2,60,08 2,6 2,5 2,5 2,5 2,5 2,4 2,4 2,4 2,3 2,30,10 2,5 2,5 2,5 2,5 2,5 2,4 2,4 2,4 2,3 2,30,11 2,7 2,7 2,6 2,6 2,6 2,6 2,5 2,5 2,5 2,40,13 2,9 2,8 2,8 2,8 2,8 2,7 2,7 2,7 2,6 2,60,14 2,9 2,9 2,9 2,9 2,8 2,8 2,8 2,7 2,7 2,6


2,0 1,8 1,7 1,6 1,5 1,4 1,4 1,4 1,3 1,4 16,8 15,6 14,5 13,7 13,1 12,6 12,2 12,2 12,2 12,52,4 2,3 2,2 2,1 2,1 2,0 2,0 2,0 2,0 2,0 22,4 21,2 20,4 19,8 19,5 19,4 19,3 19,5 19,9 20,52,6 2,5 2,4 2,3 2,3 2,3 2,3 2,4 2,5 2,5 29,5 28,3 27,7 27,0 27,1 27,3 28,0 28,8 30,6 31,93,2 3,2 3,1 3,0 3,0 3,0 3,0 3,0 3,1 3,1 46,4 45,8 45,2 45,1 45,6 45,9 46,5 47,7 48,7 50,13,8 3,8 3,7 3,6 3,6 3,6 3,6 3,6 3,6 3,6 66,7 66,6 66,0 66,2 66,0 66,4 68,0 68,7 69,6 69,64,1 4,1 4,1 3,9 3,9 3,9 4,0 4,0 3,9 3,9 80,1 80,6 80,8 79,3 79,6 80,0 83,7 84,5 82,8 83,64,5 4,5 4,5 4,4 4,4 4,4 4,3 4,4 4,3 4,3 89,0 89,1 89,2 89,6 90,0 89,7 90,2 92,2 92,9 92,75,1 5,0 5,0 5,0 4,9 4,9 4,9 4,8 4,8 4,8 94,3 94,3 94,5 94,7 94,3 94,7 96,3 96,8 97,3 99,05,7 5,7 5,7 5,5 5,5 5,4 5,3 5,2 5,2 5,1 98,8 100,1 100,2 98,9 99,1 99,2 97,4 98,0 99,7 100,25,8 5,8 5,8 5,8 5,7 5,6 5,5 5,4 5,3 5,2 99,2 99,1 99,0 100,2 100,1 99,4 99,7 98,6 98,8 99,0

N35 BLEEDING _ COMBINED EFFECT APPENDIX 6C1/2


SC- Total pore area and SC- elongation 0.8/1.0 mm -> Bleeding 1,2 2 7 5Surface 1,45 1,47 1,50 1,52 1,54 1,56 1,58 1,60 1,62 1,64

0,01 5,7 6,1 6,4 6,6 6,6 6,5 6,1 5,7 5,2 4,60,05 5,3 5,8 6,3 6,6 6,8 6,7 6,5 6,2 5,7 5,10,08 4,8 5,3 5,8 6,3 6,6 6,7 6,6 6,4 6,0 5,50,12 4,1 4,7 5,2 5,7 6,1 6,4 6,4 6,3 6,1 5,60,16 3,5 4,0 4,5 5,0 5,5 5,8 6,0 6,0 5,9 5,60,19 3,0 3,4 3,9 4,4 4,9 5,3 5,5 5,6 5,6 5,40,23 2,6 3,0 3,4 3,9 4,3 4,7 5,0 5,2 5,2 5,10,26 2,4 2,7 3,1 3,6 4,0 4,3 4,6 4,8 4,9 4,80,30 2,3 2,6 3,0 3,4 3,8 4,1 4,4 4,6 4,6 4,60,34 2,4 2,7 3,0 3,4 3,7 4,0 4,2 4,4 4,5 4,4


4,7 4,1 3,8 3,5 3,4 3,3 3,4 3,7 4,1 4,6 41,1 34,0 29,6 26,9 25,8 26,0 27,7 32,2 39,5 50,14,2 3,7 3,5 3,4 3,5 3,6 4,0 4,4 5,1 5,8 39,1 32,0 27,7 25,6 25,5 26,9 30,4 35,6 44,3 56,94,3 4,1 4,1 4,2 4,6 5,0 5,6 6,3 7,1 8,0 45,0 38,7 34,8 33,8 34,7 37,6 42,1 49,0 58,8 72,54,1 4,1 4,2 4,5 5,1 5,8 6,7 7,6 8,7 9,8 50,1 44,2 40,7 39,8 41,6 45,4 51,9 60,1 71,9 87,23,8 3,9 4,0 4,4 5,0 6,0 7,0 8,2 9,6 10,9 54,7 48,2 44,5 43,6 45,7 51,3 58,2 68,1 81,1 97,53,6 3,7 3,9 4,4 5,0 5,9 7,1 8,6 10,1 11,8 60,0 53,9 50,1 50,0 51,6 56,6 64,4 76,8 90,8 109,33,5 3,6 4,0 4,6 5,5 6,6 7,8 9,2 10,8 12,5 66,5 60,3 57,6 58,3 62,9 69,6 77,9 88,8 103,5 122,03,5 3,8 4,3 5,0 5,9 7,4 8,9 10,4 12,2 14,1 72,3 69,6 68,7 69,6 74,8 85,0 95,7 108,1 124,5 145,43,8 4,2 4,8 5,7 6,9 8,3 10,0 11,7 13,8 16,1 80,9 78,5 80,1 84,2 91,7 101,6 114,7 128,7 148,4 174,54,4 5,0 5,8 6,9 8,4 10,1 12,4 14,8 17,1 19,6 93,0 93,2 96,1 102,8 113,3 126,6 146,8 167,8 191,7 220,4

stdev min max averageSC- Total pore area and F- BET value -> Bleeding 1,1 3 7 5

Surface 1,25 2,66 4,08 5,49 6,91 8,32 9,74 11,15 12,57 13,980,01 6,9 6,2 5,6 5,1 4,6 4,2 3,9 3,6 3,4 3,30,05 7,4 6,7 6,0 5,4 4,9 4,5 4,2 3,9 3,6 3,40,08 7,5 6,8 6,2 5,6 5,1 4,7 4,3 4,0 3,7 3,50,12 7,2 6,6 6,0 5,5 5,0 4,6 4,3 4,0 3,8 3,60,16 6,8 6,2 5,7 5,2 4,8 4,5 4,2 3,9 3,7 3,50,19 6,2 5,7 5,3 4,9 4,6 4,3 4,0 3,8 3,6 3,40,23 5,6 5,2 4,9 4,6 4,3 4,0 3,8 3,6 3,5 3,30,26 5,1 4,8 4,5 4,3 4,1 3,9 3,7 3,5 3,4 3,30,30 4,7 4,5 4,3 4,1 3,9 3,8 3,6 3,5 3,4 3,30,34 4,5 4,4 4,2 4,1 3,9 3,8 3,6 3,5 3,4 3,3


4,4 3,7 3,2 2,9 2,7 2,6 2,6 2,6 2,8 3,1 32,0 29,6 28,5 28,3 29,0 30,5 32,8 36,1 41,4 47,75,1 4,3 3,7 3,4 3,2 3,1 3,0 3,2 3,4 3,7 34,4 32,0 31,1 30,9 31,9 33,8 36,3 41,0 46,4 53,76,9 6,1 5,5 5,0 4,8 4,6 4,5 4,6 4,8 5,1 46,4 44,7 44,4 45,1 46,8 49,3 52,9 57,6 63,9 72,28,0 7,2 6,6 6,2 6,0 5,9 5,8 5,9 6,1 6,4 55,6 54,6 55,2 56,4 59,3 63,3 67,7 73,7 81,1 90,28,4 7,7 7,1 6,7 6,5 6,6 6,6 6,7 7,0 7,3 62,5 61,7 62,3 64,1 67,6 73,3 79,4 85,5 94,5 104,58,7 8,0 7,5 7,2 7,0 7,0 7,1 7,3 7,6 8,1 70,9 70,0 70,6 73,1 77,1 82,6 88,9 96,8 106,5 118,19,2 8,5 8,0 7,7 7,6 7,6 7,7 7,9 8,2 8,7 82,9 81,2 81,6 84,6 88,6 94,0 100,9 109,3 118,8 130,7

10,2 9,6 9,1 8,8 8,6 8,5 8,6 9,0 9,3 9,7 100,2 99,4 100,2 102,0 105,9 110,3 116,9 128,2 137,8 149,111,5 10,9 10,5 10,2 9,9 9,9 9,9 10,4 10,6 10,7 122,0 121,0 121,8 123,8 125,7 131,2 137,0 149,0 158,4 164,513,8 12,9 12,4 12,3 12,1 12,0 12,2 12,2 12,5 12,8 153,2 148,1 147,7 152,3 155,4 159,9 167,6 174,2 184,4 194,2

WR35 BLEEDING _ COMBINED EFFECT APPENDIX 6C2/2


SC- Total pore area and SC- elongation 0.8/1.0 mm -> Bleeding 2,6 1 11 3Surface 1,45 1,47 1,50 1,52 1,54 1,56 1,58 1,60 1,62 1,64

0,01 7,6 7,5 7,5 7,6 7,8 8,2 8,8 9,5 10,3 11,20,05 5,2 5,1 5,1 5,2 5,5 5,9 6,4 7,1 7,9 8,80,08 3,5 3,4 3,4 3,6 3,8 4,1 4,6 5,2 5,9 6,80,12 2,3 2,3 2,3 2,4 2,6 2,9 3,3 3,8 4,4 5,10,16 1,7 1,6 1,7 1,7 1,9 2,1 2,4 2,8 3,3 3,80,19 1,3 1,2 1,3 1,3 1,4 1,6 1,8 2,1 2,5 3,00,23 1,0 1,0 1,0 1,1 1,2 1,3 1,5 1,7 2,1 2,40,26 0,9 0,9 0,9 1,0 1,0 1,2 1,3 1,5 1,8 2,10,30 0,9 0,9 0,9 0,9 1,0 1,1 1,2 1,4 1,6 1,90,34 0,9 0,9 0,9 1,0 1,0 1,1 1,2 1,4 1,5 1,7


7,0 5,5 4,4 4,0 4,4 6,0 8,7 13,0 19,5 28,5 45,5 36,6 29,6 26,6 28,2 36,4 49,7 68,4 94,4 127,45,2 4,1 3,3 3,0 3,1 3,9 5,4 8,1 12,4 18,8 50,0 40,0 32,4 28,6 28,4 32,9 42,1 56,6 78,3 106,34,0 3,3 2,9 2,8 2,9 3,3 4,3 6,1 8,9 13,5 57,8 48,4 41,8 39,2 38,4 40,5 47,2 58,7 75,4 99,93,2 2,7 2,5 2,4 2,6 2,9 3,7 5,0 7,1 10,3 68,5 58,7 53,8 50,1 49,3 50,6 56,2 66,1 81,3 101,32,7 2,3 2,1 2,1 2,2 2,5 3,1 4,0 5,6 8,1 81,8 71,1 64,3 60,4 58,5 59,5 64,0 72,8 85,9 104,62,3 2,0 1,8 1,8 1,9 2,2 2,7 3,5 4,8 6,9 90,2 80,5 73,6 69,0 67,2 68,6 72,8 80,8 95,1 114,71,9 1,7 1,7 1,7 1,8 2,0 2,4 3,2 4,3 6,0 94,6 85,5 80,4 77,1 76,2 77,3 81,4 91,1 103,8 123,31,8 1,6 1,6 1,6 1,7 2,0 2,4 3,0 3,9 5,4 95,9 88,6 84,3 82,2 83,7 86,5 90,6 98,4 110,7 129,71,7 1,6 1,6 1,6 1,8 2,0 2,5 3,1 4,0 5,2 95,5 90,9 89,1 87,7 89,3 92,3 100,4 110,6 123,2 141,41,8 1,7 1,7 1,8 2,0 2,3 2,7 3,2 4,1 5,2 93,0 90,5 90,6 93,3 96,5 102,5 110,4 117,3 130,9 147,6

stdev min max averageSC- Total pore area and F- BET value -> Bleeding 2,3 0 13 2

Surface 1,25 2,66 4,08 5,49 6,91 8,32 9,74 11,15 12,57 13,980,01 13,3 10,0 7,3 5,2 3,6 2,5 1,7 1,2 0,8 0,60,05 9,9 7,4 5,4 3,8 2,7 1,9 1,3 0,9 0,6 0,50,08 7,1 5,3 3,9 2,8 2,0 1,4 1,0 0,7 0,5 0,40,12 5,0 3,8 2,8 2,0 1,5 1,0 0,7 0,5 0,4 0,30,16 3,6 2,8 2,1 1,5 1,1 0,8 0,6 0,4 0,3 0,30,19 2,7 2,1 1,6 1,2 0,9 0,7 0,5 0,4 0,3 0,20,23 2,1 1,7 1,3 1,0 0,8 0,6 0,5 0,4 0,3 0,20,26 1,8 1,5 1,2 0,9 0,8 0,6 0,5 0,4 0,3 0,30,30 1,6 1,4 1,1 0,9 0,8 0,6 0,5 0,4 0,4 0,30,34 1,6 1,3 1,1 1,0 0,8 0,7 0,6 0,5 0,4 0,4


14,4 10,9 8,2 6,1 4,4 3,2 2,3 1,6 1,2 0,9 54,1 54,6 56,8 59,1 61,5 64,5 66,5 68,0 70,8 74,09,3 7,0 5,2 3,9 2,9 2,1 1,5 1,1 0,8 0,7 47,0 47,2 48,6 50,7 53,8 55,9 58,1 61,3 64,8 70,67,2 5,4 4,0 3,0 2,2 1,6 1,2 0,9 0,7 0,6 50,9 51,0 52,0 54,1 56,8 59,7 63,4 67,1 72,3 79,15,9 4,4 3,3 2,5 1,9 1,4 1,1 0,8 0,7 0,6 58,6 58,2 59,7 61,9 65,0 68,3 73,0 78,1 84,7 93,14,8 3,7 2,8 2,1 1,6 1,3 1,0 0,8 0,7 0,6 66,2 66,7 67,3 69,6 72,6 77,4 84,5 90,9 99,4 108,84,1 3,2 2,5 1,9 1,5 1,2 1,0 0,8 0,7 0,6 75,7 75,2 76,1 77,7 81,8 87,7 93,9 101,6 109,5 119,83,6 2,8 2,3 1,8 1,5 1,2 1,0 0,8 0,7 0,7 83,5 82,8 84,3 86,9 90,4 96,8 103,5 110,6 119,9 134,43,4 2,8 2,2 1,8 1,5 1,3 1,1 0,9 0,8 0,7 94,8 94,9 93,6 96,8 100,1 105,7 113,8 121,5 129,6 141,13,4 2,8 2,3 1,9 1,7 1,4 1,2 1,1 1,0 0,9 104,4 101,6 103,0 104,9 109,0 112,6 119,2 125,9 138,5 146,63,5 3,0 2,6 2,3 2,0 1,7 1,5 1,3 1,2 1,0 111,2 111,7 113,8 117,0 120,7 121,6 126,5 137,5 143,6 144,3

N30 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D1/6


SC-Flkn 3.15/4.0 mm and SC- LA value (mod.) -> Comp. Strength 2,1 42 51 46Surface 1,23 1,26 1,28 1,30 1,32 1,34 1,36 1,38 1,41 1,43

20 50,7 50,2 49,8 49,8 49,9 50,1 50,3 50,4 50,4 50,122 49,7 49,1 48,7 48,6 48,7 48,9 49,1 49,2 49,1 48,824 48,7 48,1 47,7 47,6 47,6 47,8 47,9 47,9 47,8 47,526 47,9 47,2 46,8 46,6 46,6 46,7 46,8 46,8 46,5 46,228 47,2 46,5 46,1 45,9 45,9 45,9 45,9 45,7 45,4 45,030 46,7 46,1 45,6 45,4 45,3 45,2 45,1 44,9 44,5 43,932 46,5 45,9 45,4 45,2 45,0 44,8 44,6 44,2 43,7 43,134 46,5 45,9 45,5 45,2 44,9 44,7 44,3 43,8 43,2 42,536 46,7 46,1 45,7 45,4 45,1 44,8 44,3 43,7 42,9 42,138 47,1 46,6 46,2 45,8 45,5 45,1 44,5 43,8 42,9 42,0


3,5 3,0 3,2 3,6 3,8 3,8 3,8 4,0 4,4 5,4 3,4 3,0 3,2 3,6 3,8 3,8 3,8 4,0 4,4 5,43,0 2,4 2,5 2,8 2,9 2,8 2,8 2,9 3,3 4,4 3,0 2,4 2,6 2,8 3,0 2,9 2,9 2,9 3,4 4,53,1 2,3 2,3 2,4 2,5 2,4 2,3 2,3 2,6 3,7 3,2 2,4 2,4 2,6 2,6 2,5 2,4 2,4 2,8 3,93,4 2,6 2,3 2,3 2,3 2,2 2,2 2,1 2,4 3,3 3,5 2,7 2,5 2,5 2,5 2,4 2,3 2,3 2,6 3,63,6 2,8 2,5 2,4 2,2 2,2 2,1 2,1 2,4 3,2 3,8 3,0 2,7 2,6 2,4 2,4 2,3 2,3 2,6 3,63,9 3,1 2,7 2,5 2,2 2,1 2,2 2,3 2,6 3,3 4,2 3,4 3,0 2,7 2,5 2,4 2,4 2,5 2,9 3,84,4 3,6 3,1 2,7 2,4 2,2 2,3 2,5 2,8 3,4 4,7 3,9 3,4 3,0 2,7 2,5 2,6 2,8 3,2 4,05,2 4,4 3,8 3,2 2,7 2,4 2,5 2,6 2,9 3,5 5,6 4,8 4,1 3,5 3,0 2,7 2,8 3,0 3,4 4,26,3 5,5 4,7 4,0 3,3 2,9 2,8 2,8 2,9 3,4 6,8 5,9 5,1 4,4 3,7 3,3 3,1 3,2 3,4 4,07,7 6,8 6,0 5,2 4,5 4,0 3,7 3,4 3,2 3,4 8,2 7,3 6,5 5,6 5,0 4,4 4,1 3,9 3,8 4,0

stdev min max averageSC-LA value (mod.) and SC- Pore area 60-300Å -> Comp. Strength 2,0 45 52 47

Surface 20 22 24 26 28 30 32 34 36 380,00 50,0 48,8 47,7 46,7 45,9 45,3 44,9 44,9 45,0 45,40,02 51,0 49,8 48,7 47,6 46,7 46,0 45,6 45,3 45,4 45,60,03 51,9 50,7 49,5 48,4 47,4 46,7 46,1 45,8 45,7 45,80,05 52,3 51,1 50,0 48,9 47,9 47,1 46,5 46,1 45,9 46,00,06 52,3 51,2 50,1 49,1 48,1 47,3 46,7 46,3 46,0 46,00,08 51,8 50,9 49,8 48,9 48,0 47,2 46,6 46,2 46,0 45,90,10 51,0 50,1 49,2 48,4 47,6 46,9 46,4 46,0 45,8 45,80,11 50,0 49,2 48,5 47,7 47,0 46,5 46,0 45,7 45,6 45,60,13 49,0 48,3 47,6 47,0 46,4 46,0 45,7 45,5 45,4 45,50,14 48,0 47,4 46,9 46,4 45,9 45,6 45,4 45,3 45,3 45,4


3,8 2,9 2,5 2,3 2,2 2,2 2,3 2,6 3,2 4,3 3,8 3,0 2,6 2,4 2,4 2,4 2,6 2,9 3,6 4,84,2 3,4 3,0 2,8 2,5 2,4 2,4 2,6 3,2 4,5 4,1 3,4 3,1 2,9 2,7 2,6 2,6 2,9 3,6 4,95,0 4,3 3,8 3,5 3,2 2,9 2,7 2,8 3,5 4,8 4,8 4,2 3,9 3,6 3,4 3,1 2,9 3,1 3,8 5,25,7 5,0 4,6 4,3 3,7 3,4 3,2 3,3 4,0 5,2 5,5 4,9 4,6 4,4 3,9 3,6 3,5 3,6 4,3 5,76,3 5,5 5,1 4,7 4,4 4,0 3,8 3,9 4,6 5,8 6,0 5,4 5,1 4,8 4,5 4,2 4,1 4,3 5,0 6,36,9 6,1 5,5 5,2 4,9 4,6 4,5 4,7 5,4 6,5 6,6 6,0 5,6 5,4 5,1 4,8 4,8 5,1 5,8 7,17,2 6,4 6,0 5,7 5,5 5,4 5,4 5,6 6,2 7,4 7,1 6,4 6,1 5,9 5,8 5,7 5,8 6,1 6,8 8,07,6 6,9 6,4 6,2 6,1 6,1 6,2 6,6 7,2 8,2 7,6 7,0 6,6 6,5 6,5 6,5 6,8 7,2 7,9 9,08,1 7,2 6,7 6,6 6,6 6,8 7,0 7,4 8,1 9,0 8,2 7,5 7,1 7,0 7,2 7,3 7,7 8,2 8,9 10,08,5 7,7 7,2 7,0 7,2 7,6 8,0 8,4 9,0 9,9 8,8 8,1 7,6 7,6 7,8 8,4 8,8 9,3 9,9 10,9

N35 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D2/6



20 48,7 48,5 48,7 49,0 49,6 50,1 50,6 50,9 50,9 50,722 47,9 47,7 47,8 48,2 48,7 49,2 49,6 49,9 49,9 49,724 47,2 47,0 47,1 47,4 47,9 48,4 48,8 49,0 48,9 48,626 46,7 46,4 46,5 46,8 47,2 47,7 48,0 48,1 48,0 47,628 46,3 46,1 46,2 46,4 46,8 47,1 47,4 47,4 47,2 46,730 46,2 46,0 46,0 46,3 46,6 46,8 46,9 46,9 46,5 45,932 46,3 46,1 46,1 46,3 46,6 46,7 46,7 46,5 46,0 45,434 46,6 46,4 46,5 46,6 46,8 46,8 46,7 46,4 45,8 45,036 47,1 47,0 47,0 47,1 47,2 47,1 46,9 46,4 45,7 44,938 47,7 47,6 47,6 47,7 47,7 47,6 47,3 46,7 45,9 44,9


3,4 3,1 3,1 3,3 3,4 3,6 4,0 4,5 5,2 6,5 3,5 3,2 3,2 3,3 3,4 3,6 3,9 4,4 5,2 6,42,7 2,3 2,4 2,4 2,5 2,6 2,8 3,3 4,1 5,3 2,8 2,5 2,5 2,5 2,6 2,6 2,9 3,3 4,1 5,42,6 2,1 2,1 2,1 2,1 2,1 2,2 2,5 3,2 4,5 2,7 2,3 2,2 2,2 2,2 2,2 2,3 2,6 3,3 4,62,7 2,2 2,1 2,0 2,0 2,0 2,0 2,1 2,7 3,9 2,9 2,4 2,2 2,2 2,1 2,1 2,1 2,2 2,8 4,12,9 2,3 2,2 2,1 2,0 2,1 2,1 2,1 2,5 3,5 3,1 2,5 2,3 2,2 2,1 2,2 2,2 2,2 2,6 3,73,2 2,6 2,3 2,1 2,0 2,1 2,3 2,4 2,7 3,5 3,5 2,8 2,5 2,3 2,2 2,3 2,4 2,5 2,9 3,83,7 3,0 2,5 2,2 2,0 2,2 2,4 2,7 3,0 3,8 4,0 3,3 2,8 2,4 2,2 2,3 2,6 2,9 3,3 4,14,5 3,7 3,0 2,4 2,1 2,1 2,5 2,8 3,2 3,9 4,8 4,0 3,2 2,6 2,2 2,3 2,6 3,0 3,5 4,35,8 4,8 3,9 3,1 2,5 2,4 2,5 2,9 3,3 3,9 6,1 5,1 4,2 3,3 2,7 2,5 2,7 3,1 3,6 4,37,2 6,2 5,2 4,3 3,6 3,3 3,3 3,4 3,6 4,0 7,5 6,5 5,4 4,5 3,8 3,5 3,5 3,6 3,9 4,5


Surface 20 22 24 26 28 30 32 34 36 380,00 49,7 48,8 48,0 47,4 46,9 46,7 46,6 46,8 47,2 47,70,02 50,8 49,9 49,1 48,4 47,8 47,4 47,3 47,4 47,6 48,00,03 51,7 50,8 49,9 49,2 48,5 48,1 47,8 47,8 47,9 48,20,05 52,2 51,3 50,4 49,7 49,0 48,5 48,2 48,0 48,1 48,20,06 52,2 51,4 50,6 49,8 49,1 48,6 48,2 48,0 48,0 48,10,08 51,8 51,0 50,2 49,5 48,9 48,4 48,0 47,8 47,7 47,80,10 51,0 50,3 49,6 48,9 48,4 47,9 47,6 47,4 47,3 47,30,11 50,0 49,4 48,7 48,2 47,7 47,3 47,0 46,8 46,8 46,90,13 48,9 48,4 47,8 47,4 46,9 46,6 46,4 46,3 46,3 46,50,14 48,0 47,5 47,0 46,6 46,3 46,1 45,9 45,9 46,0 46,2


3,5 2,5 2,1 2,0 2,0 2,1 2,1 2,0 2,4 3,5 3,5 2,6 2,2 2,1 2,1 2,2 2,2 2,2 2,5 3,73,7 2,8 2,5 2,4 2,3 2,3 2,3 2,3 2,6 3,7 3,6 2,8 2,5 2,5 2,4 2,5 2,4 2,4 2,8 3,94,5 3,6 3,3 3,1 3,0 2,9 2,7 2,7 3,1 4,2 4,3 3,6 3,3 3,2 3,1 3,0 2,8 2,8 3,3 4,35,2 4,4 3,9 3,7 3,6 3,5 3,3 3,3 3,6 4,7 5,0 4,3 3,9 3,8 3,7 3,6 3,4 3,4 3,8 4,85,7 5,0 4,5 4,3 4,2 4,0 3,9 4,0 4,4 5,4 5,5 4,8 4,5 4,3 4,2 4,1 4,0 4,1 4,6 5,66,2 5,5 5,0 4,8 4,7 4,6 4,6 4,8 5,2 6,2 6,0 5,4 5,0 4,9 4,8 4,8 4,8 5,0 5,5 6,56,7 5,9 5,4 5,2 5,2 5,3 5,4 5,7 6,3 7,0 6,6 5,8 5,5 5,3 5,4 5,5 5,7 6,1 6,6 7,47,1 6,2 5,8 5,7 5,8 6,0 6,3 6,6 7,1 8,0 7,1 6,3 5,9 5,9 6,1 6,4 6,7 7,1 7,6 8,57,6 6,7 6,3 6,2 6,5 6,8 7,1 7,5 8,0 8,8 7,8 6,9 6,5 6,6 6,9 7,3 7,7 8,1 8,6 9,47,9 7,0 6,6 6,6 6,9 7,5 7,9 8,3 8,9 9,7 8,3 7,4 7,0 7,1 7,5 8,1 8,6 9,1 9,7 10,5

AE30 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D3/6



20 47,2 47,2 47,6 48,1 48,8 49,4 50,0 50,3 50,3 50,122 46,6 46,6 46,9 47,4 48,1 48,7 49,2 49,4 49,4 49,124 46,1 46,1 46,4 46,9 47,5 48,0 48,5 48,6 48,5 48,226 45,9 45,8 46,1 46,5 47,1 47,5 47,9 47,9 47,7 47,328 45,8 45,8 46,0 46,4 46,8 47,2 47,4 47,4 47,0 46,530 45,9 45,9 46,1 46,4 46,8 47,1 47,1 46,9 46,5 45,832 46,3 46,3 46,4 46,7 47,0 47,1 47,0 46,7 46,1 45,334 46,8 46,8 46,9 47,2 47,3 47,4 47,1 46,7 45,9 45,036 47,5 47,5 47,6 47,8 47,9 47,8 47,4 46,8 46,0 44,938 48,2 48,3 48,4 48,5 48,5 48,3 47,8 47,1 46,1 45,0


4,0 3,8 3,9 4,0 4,1 4,2 4,6 5,2 6,0 7,0 4,2 4,1 4,1 4,1 4,2 4,3 4,6 5,2 6,0 7,02,9 2,9 2,9 3,0 3,0 3,1 3,3 3,8 4,8 6,0 3,1 3,1 3,1 3,1 3,1 3,2 3,4 3,9 4,8 6,12,6 2,5 2,6 2,5 2,5 2,5 2,6 3,0 3,8 5,1 2,8 2,7 2,8 2,7 2,6 2,6 2,7 3,1 3,9 5,32,9 2,7 2,7 2,5 2,4 2,2 2,2 2,5 3,2 4,5 3,1 2,9 2,9 2,7 2,5 2,3 2,3 2,6 3,4 4,83,4 3,1 2,8 2,6 2,4 2,2 2,2 2,4 3,0 4,2 3,7 3,4 3,1 2,8 2,5 2,4 2,4 2,6 3,2 4,54,0 3,5 3,2 2,8 2,5 2,4 2,4 2,6 3,1 4,1 4,3 3,8 3,4 3,0 2,7 2,5 2,5 2,8 3,4 4,54,6 4,0 3,6 3,1 2,8 2,6 2,7 2,9 3,4 4,2 5,0 4,4 3,8 3,4 3,0 2,8 2,9 3,1 3,7 4,65,5 4,7 4,1 3,6 3,1 3,0 3,0 3,3 3,7 4,4 5,9 5,1 4,4 3,8 3,3 3,1 3,2 3,5 4,0 4,96,6 5,7 4,9 4,4 3,9 3,6 3,6 3,7 4,0 4,5 6,9 6,0 5,2 4,6 4,1 3,8 3,8 3,9 4,3 5,08,0 7,1 6,3 5,7 5,1 4,8 4,6 4,4 4,5 4,7 8,3 7,3 6,5 5,9 5,3 4,9 4,8 4,7 4,8 5,3


Surface 20 22 24 26 28 30 32 34 36 380,00 49,0 48,3 47,7 47,2 47,0 46,9 47,0 47,4 47,8 48,40,02 50,5 49,8 49,2 48,7 48,3 48,2 48,2 48,4 48,8 49,20,03 51,8 51,1 50,5 50,0 49,6 49,4 49,3 49,4 49,6 49,90,05 52,8 52,2 51,6 51,1 50,7 50,4 50,2 50,2 50,3 50,40,06 53,3 52,8 52,2 51,7 51,3 51,0 50,8 50,7 50,6 50,70,08 53,4 52,9 52,4 51,9 51,5 51,2 50,9 50,8 50,7 50,70,10 52,9 52,5 52,1 51,7 51,3 51,0 50,8 50,6 50,5 50,50,11 52,2 51,8 51,5 51,1 50,8 50,5 50,3 50,1 50,1 50,00,13 51,3 51,0 50,6 50,3 50,1 49,9 49,7 49,6 49,5 49,50,14 50,3 50,0 49,8 49,5 49,3 49,2 49,1 49,0 49,0 49,0


4,1 3,0 2,5 2,3 2,3 2,4 2,7 3,0 3,8 4,9 4,2 3,1 2,6 2,4 2,5 2,6 2,8 3,2 3,9 5,04,4 3,4 2,8 2,5 2,4 2,4 2,6 3,0 3,8 5,1 4,3 3,4 2,8 2,5 2,4 2,5 2,7 3,1 3,9 5,25,2 4,2 3,6 3,2 2,9 2,6 2,7 3,2 4,0 5,3 5,0 4,1 3,6 3,2 2,9 2,7 2,7 3,2 4,0 5,45,9 4,9 4,4 3,9 3,6 3,2 3,1 3,5 4,3 5,6 5,6 4,7 4,2 3,8 3,5 3,2 3,1 3,5 4,3 5,56,5 5,5 4,9 4,5 4,0 3,8 3,7 4,0 4,8 6,1 6,1 5,2 4,7 4,3 3,9 3,7 3,6 3,9 4,8 6,07,0 6,0 5,3 4,8 4,6 4,4 4,4 4,8 5,5 6,7 6,5 5,6 5,0 4,7 4,4 4,3 4,3 4,7 5,4 6,67,3 6,3 5,7 5,3 5,1 5,0 5,1 5,5 6,2 7,4 6,9 6,0 5,4 5,1 4,9 4,9 5,1 5,4 6,2 7,37,7 6,7 6,0 5,7 5,6 5,7 5,9 6,4 7,2 8,3 7,4 6,5 5,9 5,6 5,5 5,7 5,9 6,4 7,2 8,38,0 6,9 6,3 6,1 6,2 6,5 6,7 7,2 8,0 9,1 7,8 6,8 6,2 6,0 6,2 6,5 6,7 7,2 8,1 9,28,4 7,2 6,7 6,6 6,9 7,2 7,6 8,1 8,9 9,9 8,3 7,2 6,7 6,7 7,0 7,3 7,7 8,2 9,1 10,1

AE35 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D4/6



20 46,3 45,8 45,6 45,6 45,9 46,3 46,7 47,0 47,1 47,122 45,5 44,9 44,7 44,7 44,9 45,2 45,6 45,8 45,9 45,824 44,9 44,2 43,9 43,9 44,1 44,3 44,6 44,8 44,8 44,626 44,4 43,8 43,4 43,3 43,4 43,6 43,8 43,8 43,7 43,528 44,2 43,6 43,2 43,0 43,1 43,1 43,2 43,1 42,9 42,530 44,3 43,6 43,2 43,0 43,0 42,9 42,8 42,6 42,2 41,732 44,5 43,9 43,5 43,3 43,1 43,0 42,7 42,4 41,8 41,234 45,1 44,5 44,1 43,8 43,6 43,3 42,9 42,4 41,7 40,936 45,8 45,2 44,9 44,5 44,2 43,8 43,3 42,6 41,8 40,938 46,6 46,1 45,8 45,4 45,1 44,6 43,9 43,1 42,2 41,1


3,6 3,2 3,1 3,2 3,1 3,2 3,3 3,5 4,2 5,2 3,8 3,4 3,4 3,5 3,4 3,4 3,5 3,8 4,4 5,62,9 2,4 2,3 2,3 2,4 2,4 2,5 2,8 3,4 4,5 3,2 2,6 2,6 2,6 2,7 2,7 2,8 3,1 3,7 4,93,2 2,5 2,3 2,2 2,2 2,2 2,3 2,5 3,0 4,0 3,6 2,9 2,6 2,5 2,5 2,5 2,6 2,8 3,4 4,53,8 3,1 2,6 2,4 2,2 2,2 2,2 2,3 2,7 3,5 4,3 3,5 3,0 2,7 2,6 2,5 2,5 2,6 3,1 4,14,4 3,5 2,9 2,5 2,2 2,1 2,1 2,1 2,4 3,1 4,9 4,1 3,3 2,9 2,6 2,4 2,4 2,5 2,8 3,74,6 3,9 3,2 2,6 2,3 2,1 2,1 2,1 2,3 2,9 5,3 4,4 3,7 3,1 2,6 2,5 2,4 2,5 2,7 3,55,0 4,1 3,3 2,8 2,3 2,1 2,2 2,3 2,6 3,1 5,6 4,7 3,8 3,2 2,7 2,5 2,5 2,8 3,1 3,75,6 4,6 3,8 3,1 2,5 2,3 2,3 2,5 2,8 3,2 6,2 5,2 4,3 3,5 2,9 2,6 2,7 3,0 3,3 3,96,6 5,5 4,6 3,8 3,1 2,7 2,7 2,7 2,9 3,2 7,3 6,1 5,1 4,3 3,5 3,1 3,1 3,2 3,4 3,98,0 6,9 5,9 5,0 4,2 3,7 3,4 3,3 3,2 3,3 8,6 7,5 6,5 5,5 4,7 4,2 3,9 3,8 3,8 4,0


Surface 20 22 24 26 28 30 32 34 36 380,00 46,0 45,0 44,1 43,5 43,1 42,9 43,1 43,5 44,1 44,90,02 47,4 46,4 45,5 44,8 44,4 44,2 44,2 44,5 45,0 45,60,03 48,7 47,7 46,9 46,2 45,7 45,4 45,3 45,5 45,8 46,40,05 49,7 48,8 48,0 47,3 46,8 46,4 46,3 46,3 46,6 47,00,06 50,2 49,4 48,7 48,0 47,5 47,1 47,0 47,0 47,1 47,40,08 50,3 49,6 48,9 48,3 47,8 47,5 47,3 47,3 47,3 47,60,10 49,9 49,3 48,7 48,2 47,8 47,5 47,3 47,2 47,3 47,50,11 49,2 48,7 48,2 47,7 47,4 47,2 47,0 47,0 47,1 47,30,13 48,3 47,9 47,5 47,1 46,8 46,7 46,6 46,7 46,8 47,00,14 47,5 47,1 46,7 46,5 46,3 46,2 46,2 46,3 46,5 46,7


3,2 2,4 2,2 2,2 2,2 2,2 2,2 2,4 2,9 4,0 3,4 2,7 2,5 2,5 2,5 2,5 2,6 2,8 3,3 4,53,5 2,8 2,7 2,7 2,5 2,4 2,3 2,3 2,9 4,1 3,7 3,1 3,0 3,0 2,9 2,7 2,6 2,6 3,2 4,44,3 3,8 3,7 3,6 3,4 3,0 2,6 2,5 3,0 4,2 4,4 4,0 3,9 3,9 3,7 3,4 2,9 2,7 3,2 4,55,2 4,7 4,6 4,4 4,1 3,7 3,3 3,0 3,4 4,5 5,2 4,8 4,8 4,6 4,4 4,0 3,5 3,2 3,6 4,85,8 5,3 5,2 5,1 4,8 4,4 3,9 3,7 4,0 5,1 5,7 5,4 5,3 5,3 5,0 4,6 4,2 3,9 4,2 5,46,6 6,0 5,6 5,4 5,2 4,9 4,5 4,4 4,8 5,8 6,5 6,0 5,7 5,6 5,4 5,1 4,8 4,7 5,0 6,16,9 6,2 5,9 5,7 5,6 5,4 5,2 5,3 5,7 6,6 6,9 6,3 6,1 5,9 5,8 5,7 5,5 5,6 6,0 6,97,4 6,6 6,3 6,1 6,1 6,0 6,0 6,1 6,5 7,4 7,5 6,8 6,5 6,4 6,5 6,4 6,4 6,5 6,9 7,87,6 6,9 6,5 6,4 6,5 6,6 6,7 6,9 7,4 8,3 7,9 7,2 6,9 6,8 7,0 7,1 7,2 7,4 7,9 8,88,0 7,4 7,0 6,9 7,1 7,3 7,5 7,8 8,4 9,2 8,4 7,9 7,5 7,5 7,6 7,9 8,2 8,4 9,0 9,9

WR30 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D5/6



20 62,5 62,1 61,6 61,1 60,7 60,3 60,0 59,5 59,1 58,522 61,6 61,3 61,0 60,7 60,5 60,2 60,0 59,6 59,1 58,424 60,5 60,4 60,3 60,2 60,1 60,0 59,8 59,4 58,9 58,226 59,3 59,4 59,5 59,6 59,6 59,6 59,4 59,0 58,4 57,628 58,1 58,3 58,6 58,8 59,0 59,0 58,8 58,4 57,8 56,830 56,9 57,3 57,7 58,0 58,3 58,3 58,1 57,7 56,9 55,832 55,8 56,3 56,8 57,2 57,5 57,5 57,3 56,7 55,8 54,634 54,7 55,3 55,9 56,4 56,6 56,6 56,3 55,6 54,6 53,236 53,8 54,5 55,1 55,5 55,8 55,7 55,3 54,4 53,2 51,838 53,0 53,7 54,3 54,8 54,9 54,7 54,2 53,2 51,9 50,3


4,0 3,6 3,6 3,7 3,8 3,9 3,9 4,2 4,7 5,7 3,2 2,9 2,9 3,0 3,1 3,2 3,3 3,5 3,9 4,92,9 2,6 2,6 2,7 2,8 2,9 3,0 3,2 3,8 5,0 2,4 2,2 2,2 2,3 2,3 2,4 2,5 2,7 3,3 4,22,6 2,3 2,3 2,4 2,5 2,5 2,7 2,9 3,5 4,6 2,2 1,9 1,9 2,0 2,0 2,1 2,2 2,4 2,9 4,02,9 2,5 2,4 2,3 2,3 2,4 2,6 2,8 3,3 4,5 2,4 2,1 2,0 2,0 2,0 2,0 2,2 2,4 2,9 3,93,4 3,0 2,7 2,5 2,4 2,5 2,6 2,9 3,4 4,7 2,9 2,6 2,3 2,1 2,1 2,1 2,2 2,5 3,0 4,14,1 3,6 3,1 2,7 2,5 2,5 2,7 3,0 3,6 4,8 3,6 3,1 2,7 2,3 2,1 2,2 2,4 2,6 3,1 4,35,0 4,2 3,5 2,8 2,5 2,5 2,8 3,0 3,5 4,6 4,5 3,7 3,0 2,5 2,2 2,2 2,4 2,6 3,1 4,26,1 5,0 4,0 3,2 2,8 2,8 2,9 3,0 3,3 4,2 5,6 4,6 3,6 2,9 2,5 2,5 2,6 2,7 3,0 3,97,3 6,2 5,1 4,4 4,0 3,8 3,7 3,4 3,3 3,8 6,8 5,7 4,6 4,0 3,6 3,4 3,3 3,2 3,1 3,78,7 7,7 6,7 6,0 5,6 5,5 5,2 4,7 4,1 4,0 8,2 7,1 6,2 5,4 5,1 5,0 4,8 4,4 3,9 4,0


Surface 20 22 24 26 28 30 32 34 36 380,00 60,6 60,4 60,1 59,6 59,0 58,3 57,5 56,7 55,8 54,90,02 61,1 60,8 60,4 59,7 59,0 58,1 57,2 56,2 55,2 54,30,03 61,3 60,9 60,3 59,6 58,7 57,7 56,6 55,6 54,5 53,40,05 61,3 60,8 60,1 59,2 58,2 57,1 56,0 54,8 53,7 52,60,06 61,1 60,5 59,7 58,8 57,7 56,6 55,3 54,1 53,0 51,90,08 60,8 60,2 59,4 58,4 57,3 56,1 54,9 53,7 52,5 51,40,10 60,5 59,9 59,1 58,2 57,1 55,9 54,7 53,4 52,3 51,20,11 60,3 59,7 59,0 58,1 57,0 55,9 54,7 53,5 52,4 51,30,13 60,0 59,5 58,9 58,0 57,1 56,0 54,9 53,8 52,7 51,70,14 59,7 59,3 58,8 58,0 57,2 56,2 55,2 54,1 53,1 52,2


3,9 2,9 2,5 2,4 2,4 2,5 2,5 2,7 3,8 5,7 3,2 2,4 2,1 2,0 2,0 2,1 2,1 2,4 3,4 5,24,3 3,3 2,7 2,6 2,6 2,6 2,7 3,0 4,2 5,9 3,5 2,7 2,3 2,2 2,2 2,3 2,4 2,7 3,8 5,54,9 3,9 3,2 2,9 2,9 3,0 3,2 3,6 4,7 6,5 4,0 3,2 2,6 2,4 2,5 2,6 2,8 3,2 4,3 6,05,7 4,5 3,8 3,5 3,5 3,6 3,8 4,3 5,4 7,1 4,6 3,7 3,2 3,0 3,0 3,2 3,4 3,9 5,0 6,76,3 5,2 4,4 4,1 4,1 4,3 4,5 5,0 6,0 7,7 5,2 4,3 3,7 3,5 3,6 3,8 4,1 4,7 5,7 7,46,9 5,8 5,0 4,7 4,8 5,0 5,4 6,0 6,9 8,4 5,7 4,8 4,2 4,0 4,2 4,5 5,0 5,6 6,6 8,27,4 6,3 5,6 5,3 5,5 5,8 6,3 7,0 7,9 9,3 6,1 5,2 4,7 4,6 4,8 5,2 5,8 6,5 7,6 9,07,8 6,7 6,0 5,9 6,1 6,5 7,1 7,9 8,7 10,0 6,5 5,6 5,1 5,1 5,4 5,8 6,5 7,3 8,4 9,78,3 7,2 6,5 6,4 6,8 7,4 7,9 8,6 9,5 10,6 6,9 6,0 5,5 5,5 5,9 6,6 7,2 8,0 9,0 10,38,7 7,6 7,1 7,1 7,5 8,0 8,7 9,4 10,2 11,3 7,3 6,4 6,1 6,1 6,6 7,2 7,9 8,7 9,6 10,8

WR35 COMPRESSIVE STRENGTH_COMBINED EFFECT APPENDIX 6D6/6



20 61,8 61,2 60,6 60,1 59,7 59,4 59,2 59,0 58,8 58,622 60,8 60,3 59,9 59,6 59,4 59,3 59,2 59,2 59,0 58,724 59,5 59,3 59,1 59,0 59,0 59,0 59,1 59,1 59,0 58,726 58,2 58,2 58,2 58,3 58,5 58,7 58,9 58,9 58,8 58,428 56,9 57,0 57,3 57,6 57,9 58,2 58,5 58,5 58,4 57,930 55,7 56,0 56,4 56,8 57,3 57,7 58,0 58,0 57,8 57,232 54,5 55,0 55,5 56,1 56,7 57,1 57,4 57,4 57,0 56,434 53,5 54,1 54,7 55,4 56,0 56,5 56,7 56,6 56,1 55,436 52,6 53,3 54,0 54,8 55,4 55,8 55,9 55,7 55,1 54,338 51,9 52,7 53,5 54,2 54,8 55,1 55,1 54,8 54,1 53,1


4,4 3,9 3,7 3,6 3,4 3,5 3,6 3,9 4,6 5,7 3,5 3,2 3,0 3,0 2,9 2,9 3,0 3,3 3,9 4,93,3 2,8 2,7 2,6 2,5 2,5 2,7 3,0 3,7 4,9 2,7 2,3 2,2 2,2 2,1 2,1 2,3 2,5 3,2 4,22,8 2,4 2,2 2,1 2,1 2,2 2,3 2,5 3,1 4,3 2,3 2,0 1,9 1,8 1,8 1,8 1,9 2,1 2,6 3,73,0 2,5 2,3 2,2 2,1 2,2 2,3 2,4 2,8 3,9 2,6 2,2 2,0 1,9 1,8 1,8 1,9 2,0 2,4 3,33,6 3,0 2,7 2,5 2,4 2,4 2,4 2,4 2,6 3,6 3,1 2,7 2,4 2,2 2,1 2,1 2,1 2,0 2,2 3,14,4 3,8 3,3 3,0 2,8 2,8 2,7 2,6 2,7 3,7 4,0 3,4 2,9 2,6 2,4 2,4 2,4 2,2 2,4 3,25,3 4,6 4,0 3,5 3,3 3,2 3,2 3,0 3,1 3,9 4,9 4,2 3,6 3,2 2,9 2,8 2,8 2,6 2,7 3,56,5 5,7 4,9 4,3 4,0 3,9 3,8 3,7 3,7 4,3 6,1 5,2 4,5 3,9 3,6 3,4 3,3 3,2 3,3 3,98,0 7,1 6,2 5,5 5,1 4,9 4,6 4,4 4,4 4,8 7,6 6,6 5,8 5,1 4,6 4,3 4,2 4,0 4,0 4,49,3 8,4 7,6 7,1 6,5 6,3 6,0 5,6 5,3 5,5 9,0 8,0 7,1 6,5 6,0 5,7 5,4 5,1 4,9 5,2


Surface 20 22 24 26 28 30 32 34 36 380,00 59,6 59,4 59,0 58,6 58,0 57,5 56,8 56,2 55,6 54,90,02 60,1 59,8 59,3 58,7 58,0 57,3 56,6 55,8 55,0 54,30,03 60,4 59,9 59,3 58,6 57,8 56,9 56,0 55,2 54,3 53,50,05 60,4 59,8 59,1 58,3 57,4 56,4 55,4 54,5 53,5 52,70,06 60,2 59,6 58,9 58,0 57,0 55,9 54,8 53,8 52,8 51,90,08 60,0 59,4 58,6 57,6 56,6 55,5 54,4 53,3 52,3 51,40,10 59,7 59,1 58,4 57,4 56,4 55,3 54,2 53,1 52,1 51,20,11 59,5 59,0 58,2 57,3 56,3 55,3 54,2 53,2 52,2 51,30,13 59,3 58,8 58,2 57,4 56,4 55,4 54,4 53,4 52,4 51,60,14 59,0 58,6 58,1 57,4 56,6 55,7 54,7 53,8 52,9 52,0


3,5 2,5 2,1 2,1 2,4 2,7 3,2 3,9 4,9 6,5 2,9 2,1 1,8 1,8 2,1 2,4 2,8 3,5 4,4 5,93,8 2,8 2,3 2,3 2,6 3,0 3,5 4,2 5,2 6,7 3,2 2,4 2,0 2,0 2,2 2,6 3,1 3,7 4,8 6,14,5 3,4 2,7 2,6 2,8 3,3 3,9 4,6 5,7 7,1 3,7 2,8 2,3 2,2 2,5 2,9 3,5 4,2 5,2 6,65,2 4,0 3,4 3,1 3,3 3,8 4,4 5,2 6,3 7,6 4,3 3,3 2,8 2,7 2,9 3,4 4,0 4,8 5,9 7,25,8 4,7 3,9 3,6 3,9 4,4 5,1 5,9 6,8 8,2 4,8 3,9 3,3 3,1 3,4 3,9 4,6 5,5 6,5 7,86,5 5,3 4,5 4,2 4,5 5,1 5,8 6,6 7,5 8,8 5,4 4,4 3,8 3,6 3,9 4,6 5,3 6,2 7,2 8,57,0 5,8 5,0 4,8 5,1 5,8 6,7 7,4 8,4 9,7 5,8 4,9 4,3 4,2 4,6 5,3 6,1 7,0 8,1 9,57,5 6,3 5,5 5,3 5,8 6,6 7,4 8,2 9,2 10,3 6,3 5,4 4,7 4,6 5,2 6,0 6,8 7,7 8,8 10,17,8 6,6 5,9 6,0 6,5 7,3 8,2 9,0 9,9 11,0 6,5 5,6 5,0 5,2 5,8 6,6 7,5 8,5 9,5 10,68,1 6,9 6,3 6,4 7,0 8,0 8,9 9,8 10,7 11,6 6,8 5,9 5,4 5,6 6,2 7,2 8,1 9,1 10,1 11,2

iii - Aaltodoc

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