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Structural Health Monitoring of Concrete Structures Submitted
for CE694 (Seminar)
Master of Technology
in
STRUCTURAL ENGINEERING
Submitted by
Souradeep Sen
(Roll No. 143040044)
Under the supervision
of
Prof. Sauvik Banerjee
Department of Civil Engineering
Indian Institute of Technology Bombay
November, 2014
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Abstract
Structural Health Monitoring is the field where the health of
structures are assessed, monitored and evaluated from time to time,
for the improvement in serviceability of these structures. In this
seminar report, a basic understanding of the meaning, need, and
methods of structural health monitoring (SHM) has been presented.
Also, the SHM of concrete structures have been mentioned and case
studies regarding these have been presented in a concise
manner.
Methods regarding the uses of piezoceramic based sensors,
Interferometric methods of frequency evaluation and self-diagnosis
materials for structural health monitoring have been presented
case-wise from the works of various authors. The results from the
experiments conducted for the respective methods have been shown
and discussed.
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Acknowledgement
My indebtedness to Prof. Sauvik Banerjee is unlimited for his
kind guidance, co-operation and help in selection of the topic,
describing the topic in detail and presenting a large number of
study materials which helped me in completion of this seminar
report.
Souradeep Sen
Roll No. 143040044
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Structural Health Monitoring of Concrete Structures
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Table of Contents
Introduction to Structural Health
Monitoring...........................................................................
3 Case Studies
...................................................................................................................................
5
1) Wen-I Liao et al. (Structural health monitoring of concrete
columns subjected to seismic excitations using piezoceramic-based
sensors).
..........................................................................
5
Introduction
.............................................................................................................................
5 The Specimen
..........................................................................................................................
6 Method of measuring the Energy and Damage Index used in the
experiment ........................ 9 Results after the shake table
test
............................................................................................
10
2) F.C. Ponzo et al. (Structural Health Monitoring of Reinforced
Concrete Structures using Nonlinear Interferometric Analysis).
........................................................................................
12
Introduction
...........................................................................................................................
12
The Simplified Method Proposal Method Proposed by Ponzo et al.
..................................... 13
Frequency and Damping Cofficient by IRF, Interferometric and
S-transform Methods ...... 14
3) H. Inada et al. (Improvement on Health Monitoring System
Using Self-diagnosis Materials for Practical Application).
.........................................................................................................
19
Introduction
...........................................................................................................................
19 Self-Diagnosis Materials and their Production
......................................................................
20
Displacement measuring device using the Self-Diagnosis Materials
.................................... 22
Conclusions
..................................................................................................................................
25
References
....................................................................................................................................
27
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Lists of Figures
Figure 1 Configuration of the tested column specimen in lateral
view and front view. ................ 6 Figure 2 Test setup of the
shake table test.
....................................................................................
7 Figure 3 TCU078 seismogram of the 1999
....................................................................................
8 Figure 4 Location of the smart aggregates
.....................................................................................
8 Figure 5 Damage Pattern of PGA 600 gal and 900 gal
................................................................ 10
Figure 6 Sinusoidal wave response after the earthquake excitation
at different PGA levels ...... 10 Figure 7 Damage index matrix of
sensors after the earthquake excitation at different PGA
levels.......................................................................................................................................................
11 Figure 8 (a) Results obtained by using the station located at
the top floor as a reference station (b) Impulse Response Function
obtained by using the bottom floor as a reference station (from
Snieder and Safak (2006))
............................................................................................................
14 Figure 9 (Left) Numerical Model (Right) Input used for the
analyses ........................................ 15 Figure 10 IRFs
evaluated using the bottom floor as a reference station
...................................... 16 Figure 11 Nonlinear
Interferometric Analysis performed on the top floor accelerometric
recording and S-Transform evaluated on the single IRF
.............................................................. 18
Figure 12 Schematic drawing of materials.
.................................................................................
20 Figure 13 Two types of sensors.
..................................................................................................
20 Figure 14 Result of tensile test and regression
............................................................................
21 Figure 15 Manufacturing Process
................................................................................................
22 Figure 16 Displacement of measuring device
..............................................................................
23 Figure 17 Measurement of Displacement during Shake-Table Tests
.......................................... 24 Figure 18 Examples of
test results.
..............................................................................................
24
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Chapter 1
Introduction to Structural Health Monitoring
The process of implementing a damage detection and
characterization strategy for engineering structures is referred to
as Structural Health Monitoring (SHM). Here damage is defined as
changes to the material and/or geometric properties of a structural
system, including changes to the boundary conditions and system
connectivity, which adversely affect the systems performance. The
SHM process involves the observation of a system over time using
periodically sampled dynamic response measurements from an array of
sensors, the extraction of damage-sensitive features from these
measurements, and the statistical analysis of these features to
determine the current state of system health. For long term SHM,
the output of this process is periodically updated information
regarding the ability of the structure to perform its intended
function in light of the inevitable aging and degradation resulting
from operational environments. After extreme events, such as
earthquakes or blast loading, SHM is used for rapid condition
screening and aims to provide, in near real time, reliable
information regarding the integrity of the structure
Structural Health Monitoring (SHM) aims to give, at every moment
during the life of a structure, a diagnosis of the state of the
constituent materials, of the different parts, and of the full
assembly of these parts constituting the structure as a whole. The
state of the structure must remain in the domain specified in the
design, although this can be altered by normal aging due to usage,
by the action of the environment, and by accidental events. Thanks
to the time-dimension of monitoring, which makes it possible to
consider the full history database of the structure, and with the
help of Usage Monitoring, it can also provide a prognosis(evolution
of damage, residual life, etc.). If we consider only the first
function, the diagnosis, we could estimate that Structural Health
Monitoring is a new and improved way to make a Non- Destructive
Evaluation. This is partially true, but SHM is much more. It
involves the integration of sensors, possibly smart materials, data
transmission, computational power, and processing ability inside
the structures. It makes it
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possible to reconsider the design of the structure and the full
management of the structure itself and of the structure considered
as a part of wider systems.
In this seminar report, the methods of Structural Health
Monitoring for Concrete Structures will be discussed, via various
case studies accomplished by various institutes and people across
the globe. The various methods have been successfully tested to be
useful for the health monitoring of structures and have been
presented in a compact manner.
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Chapter 2
Case Studies
1) Wen-I Liao et al. Structural health monitoring of concrete
columns subjected to seismic excitations using piezoceramic-based
sensors
Introduction
Throughout the life cycle of a concrete structure, important
issues have to be addressed properly to ensure the safe operation
of these structures. It becomes essential to perform structural
health monitoring on these structures that can detect the amount of
damage on these structures. After earthquake, it becomes very
essential to monitor the health of the structure due to the
earthquakes catastrophic nature. In recent years, piezoelectric
materials have been successfully applied to the structural health
monitoring of concrete structures due to their advantages of active
sensing, low cost, quick response, availability in different
shapes, and simplicity in implementation.
There are two major categories of piezoelectric-based health
monitoring. The first includes the impedance-based approach, in
which the impedance of piezoelectric transducers can be applied to
the health monitoring of structures. Although, it is difficult to
identify the distribution and severity of a large damaged region,
the advantage of the impedance-
based approach is that it does not require knowledge of the
modal parameters or other failure information of the structure.The
second approach is the wave-based health monitoring approach
(Okafor et al 1996, Saafi and Sayyah 2001), in which the
wave-propagation properties are studied to detect and evaluate the
cracks and damages inside concrete structures. Wang et al (2001)
studied the debonding behaviors between steel rebar and concrete by
using PZT (lead zirconate titanate) patches fixed on the rebar. Sun
et al (2008) used a PZT patch transducer to
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Structural Health Monitoring of Concrete Structures
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initiate and receive elastic waves in the concrete, and obtained
the modulus of elasticity by utilizing the wave-propagation
characteristics.
Song et al (2004, 2007, 2008) developed smart aggregate (SA), an
innovative multifunctional piezoceramic-based device, to perform
structural health monitoring for concrete structures. The smart
aggregates have been successfully utilized in the structural health
monitoring of a two-story concrete frame structure (Gu et al
2007).
The piezoceramic based smart aggregates were distributed at
strategic location prior to the casting of the column for the
formation of an active sensing system for the health monitoring
of
the column. A shake table was used to simulate the earthquake
ground motion recorded in the Taiwan 1999 earthquake. The
acceleration was increased gradually upto failure of the column.
During the tests, the distributed smart aggregates and PZT patches
embedded in the concrete columns were utilised to perform the
structural health monitoring. One of the PZTs was used as an
actuator for generating and propagating of waves and the others
were used as sensors to detect the waves. If the propagation energy
was attenuated at certain portions, it meant that the portion had
cracks or voids. The decreased value of the transmission energy is
proportional to the severity and extent of the damage.
Figure 1 Configuration of the tested column specimen in lateral
view and front view.
The Specimen
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Figure 2 Test setup of the shake table test.
A flexural governed reinforced concrete column was tested on a
shake table (Figure 2). The height and cross section of the column
were 80 cm and 20 20 cm2, respectively. The rebar arrangement of
the test columns was 4#3 longitudinal steel rebars with #3@10 cm
stirrup. The average concrete strength was 210 kg cm2 and the
average yielding stresses for #3 rebar was 4100 kg cm2. The size
of the top concrete
plate was 160 160
20 cm3 and the size of the reinforced concrete foundation was
120 50 40 cm3. The foundation was designed to remain elastic when
the failure of the reinforced concrete column occurred. The
horizontal displacements and response accelerations of the column
were measured by linear variable displacement transducers (LVDTs)
and accelerometers. A total mass of 1000 kg was put on the top
plate in order to increase the inertia force. Two load cells were
placed underneath the foundation to measure the vertical load and
base shear during the test. Furthermore, in order to prevent the
abrupt falling of the top concrete plate and additional lead blocks
due to failure of the reinforced concrete column, four steel
columns were provided in the four corners of concrete mass block
for support. The input acceleration time history for all shake
table tests is the E-W component of the record at TCU078 station of
the 1999 Taiwan Chi- Chi earthquake (denoted as TCU078EW). Figure 3
below shows the seismogram of the acceleration time history.
Corresponding to the input ground motion, the test
protocol is sequentially the peak ground acceleration (PGA) of
50 gal, 200 gal, 400 gal, 600 gal, and 900 gal, and the specimen
failed at the test run of PGA=900gal.
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Figure 3 TCU078 seismogram of the 1999
In this test, PZT-type piezoceramic patches were embedded in a 1
inch cube of concrete (called smart aggregate). The piezoelectric
strain constat d33 and piezoelectric voltage constants g33 of the
PZT patches are 390 1012 C N1 and 24 103 V mN1, respectively.
Piezoceramic patches are usually fragile and can be easily damaged
by the vibrations subjected to the column. To protect the
peizeoelectric transducers, the patch is coated with insulation in
order to prevent moisture related damages. Only then it is embedded
in the concrete block as a smart aggregate.
Figure 4 Location of the smart aggregates
The sensors were kept at the specific locations as it was
speculated that damage would most likely occur at the places which
have a tendency of formation of a plastic hinges. The actuator was
kept at the centre of the column. (Figure 4)
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Method of measuring the Energy and Damage Index used in the
experiment Wavelet packet analysis was used as a signal processing
tool in order to analyse the signals detected by the smart
aggregates. The sensor signal (say S) was decomposed by an n-level
wavelet packet decomposing into 2n signal sets{X1, X2,., X2n }. If
Eij is the energy of the decomposed signal, here i stands for time
index and j for the frequency band XJ = [xj,1, xj,2, . . . , xj,m ]
where m is the sampling data.
The energy vector at time index i can be given as Ei = [Ei,1,
Ei,2, . . . , Ei,2n ].
Root-mean-square deviation (RMSD) is a commonly used damage
index to compare the difference between the signatures of the
healthy state and damaged state. In the proposed approach, the
damage index is formed by calculating the RMSD between the energy
vectors of the healthy state and the damaged state. The energy
vector for healthy data is Eh = [Eh,1, Eh,2, . . . , Eh,2n ]. The
energy vector Ei for the damaged state at time index i is defined
as Ei = [Ei,1, Ei,2, . . . , Ei,2n ]. The damage index (DI) at time
i is defined as:
The proposed damage index represents the transmission energy
loss portion caused by damage. When the damage index is close to 0,
it means that the concrete structure is in a healthy state. The
greater the damage index, the more serious the damage is.
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Results after the shake table test
Figure 5 Damage Pattern of PGA 600 gal and 900 gal
The shake table test causes the column to crack at the bottom
and top. Figure 5 shows the failure pattern of the column specimen
after the PGA runs near 600 to 900 gal. After this, the smart
aggregates were removed for the structural health monitoring during
and after the shake table test. From the sensor voltage graph
(Figure 6) of the PZT-S2 used as the smart aggregate, it was seen
that the peak of the peak sensor voltage decreased as the ground
acceleration of the shake table kept increasing. This means that
the propagated energy kept getting attenuated higher as the cracks
in the column kept increasing in size and number.
Figure 6 Sinusoidal wave response after the earthquake
excitation at different PGA levels
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The plot of the damage index matrix of sensors is shown in
figure , and the values are demonstrated in Table 1 with the
measured drift ratio of the column for comparison, where the drift
ratio is defined as the relative displacement at the column top to
the column bottom divided by the column height. After each test run
of the shake table test, the sensor signals for health monitoring
have been measured twice by the same sweep sine excitation. The
damage indices shown in Table 1 were the mean values of the first
and second measuring. The value shown in brackets after the damage
index was the relative error between each measuring.
Figure 7 Damage index matrix of sensors after the earthquake
excitation at different PGA levels
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2) F.C. Ponzo et al. Structural Health Monitoring of Reinforced
Concrete Structures using Nonlinear Interferometric Analysis
Introduction The maintenance of a huge number of aged structures
and infrastructures require a huge effort and present a huge task
to accomplish, if a detailed analysis of seismic risk is to be done
properly. It was seen that periodic visual inspection was rendering
to be more and more ineffective. A specific task of the Italian
research RELUIS-DPC 2010-2013 project, funded by the Department of
Italian Civil Protection (DPC), dealt with the devising and
implementing of a fast procedure (Ponzo et al., 2010) to obtain
useful information about the damage evolution in a large number of
strategic buildings during and after seismic events, by the help of
just a few sensors. The two most important goal which to be
obtained regarding the usage of sensors was feasibility and cost
optimisation, so that it could be used in a more widespread manner.
Significant research in the field of Non-destructive Damage
Evaluation (NDE) methods has been done which have made use of the
dynamic response changes in the structure. (Chen et al., 1995;
Capecchi and Vestroni, 1999; Ponzo et al., 2010)
NDE methods are classified into Four Levels, depending on the
amount of data that can be retrieved from the sensors.
(i) Level I methods, i.e. those methods that only identify if
damage has occurred. (ii) Level II methods, i.e. those methods that
identify if damage has occurred and
simultaneously determine the location of damage.
(iii) Level III methods, i.e. those methods that identify if
damage has occurred, determine the location of damage as well as
estimate the severity of damage.
(iv) Level IV methods, i.e. those methods that identify if
damage has occurred, determine the location of damage, estimate the
severity of damage and evaluate the impact of damage on the
structure.
With the increase in level, the amount of data and algorithm
required became more sophisticated. And so did the cost. Ponzo et
al. (2010) had proposed an innovative approach for a simplified
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structural damage detection. Due to this, it was easier to
obtain information about the health of the structure minutes after
a seismic activity. The method includes the acquisition of
structural dynamic response by a three-directional accelerometer
which is to be installed on top floor of a structure. From the
data, maximum acceleration, frequency variation and equivalent
viscous damping can be obtained and hence be used to find the
maximum inter-storey displacement.
The paper focused on the interferometric analyses (Snieder and
Safak, 2006; Picozzi et al., 2011) useful to obtain the dynamic
response of the monitored structure. Particularly, the Impulse
Response Function (IMF) obtained by mean the interferometric
analysis, applied on the data recorded on the monitored structure,
was combined with the S-Transform (Stockwell et al., 1996) to
perform a pseudo time-frequency analysis with the aim to automatize
the procedure to evaluate both frequency and damping variation
during earthquakes.
The Simplified Method Proposal Method Proposed by Ponzo et
al.
The method by Ponzo et al. (2010) began from a limited number of
records obtained from the accelerometer on the top floor of the
structure. It overcame certain limitations of Level I
methods. The method considers some parameters evaluated by the
recorded signals: (i) Maximum Absolute Top Acceleration (MATA);
(ii) variations of the fundamental frequency (iii) variation of the
equivalent viscous damping, and provides a combination of these
parameters to estimate the maximum interstorey drift by means of an
empirical relationship. All signals are filtered with band-pass
filter centred on the fundamental frequency of the monitored
structure.
The Maximum Absolute Top Acceleration represents the first
instrumental parameter. It was evaluated directy by the filtered
signal (filtered by band filter) recorded by the accelerometer. An
appropriate arrangement of recording sensors on the structure
permits to reconstruct all displacement and rotation components of
the floor. The other two instrumental parameters
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considered in the method are the percent variations (f1) between
the fundamental frequency of the building before the seismic event
finit and the minimum one fmin, corresponding to the maximum non
linear behaviour of the building and the percent variations (f2)
between initial and final frequency (ffin)
The frequencies were evaluated by using a STFT (Short Time
Fourier Transform) applied to the signals. The final instrumental
parameter considered is the variation of the equivalent structural
viscous damping related to the first mode of vibration of the
structure. For non-stationary signals, the damping can be found by
the semi-probabilistic approach elaborated by Mucciarelli and
Gallipoli (2007).
Frequency and Damping Cofficient by IRF, Interferometric and
S-transform Methods
The structure which was selected is as shown in Figure 9. It had
5 storeys, with inter-storey height of 3m, representative of
Italian Standard Buildings. The building considered had a plan of
12 m x 15m.In order to take into account the presence of infill
panels within the structural R/C frames and their interaction with
the columns, both the masonry strength and stiffness contribution
had been considered by inserting two equivalent structural elements
in the models. The mechanical characteristics of these elements
were evaluated considering the Mainstone model (Mainstone,
1974).
Figure 8 (a) Results obtained by using the station located at
the top floor as a reference station (b) Impulse Response Function
obtained by using the bottom floor as a reference station (from
Snieder and Safak (2006))
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It was noted that using the top floor as a reference station it
was possible to retrieve information about the wavefield propagated
into the building while using the bottom floor as a reference
station it was possible to extract the impulse response function of
the building.( After deconvolution of the response in frequency
domain)
Figure 9 (Left) Numerical Model (Right) Input used for the
analyses
The damping can be evaluated before and after earthquake using
the IRF (Impulse Response Function). Figure 10 shows the example of
an IRF evaluated with bottom floor as reference. By using
logarithmic decrement method, the viscous damping can be
evaluated.
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Figure 10 IRFs evaluated using the bottom floor as a reference
station
It was noted that from the IRFs evaluated at the top floor it
was also possible to extract interesting information related to the
fundamental frequency of the structure. In fact, comparing the
results obtained from the fundamental frequency of the structure
before and after the earthquake it was possible to note a shift of
the frequency from 2.0Hz (before the earthquake) to 1.10Hz (after
the earthquake). Applying the logarithm decrement method on the
evaluated IRFs before and after the earthquake it was seen that the
equivalent viscous damping factor varied from 5.22% (before the
earthquake) to 9.40% (after the earthquake).
Picozzi et al. (2011) showed that it was possible to evaluate
the IRF also from a windowed signal acting on a single time-window.
It was seen that the time-varying behavior of the structure of the
structure could be represented as frequency variation using both
the IRFs evaluated from windowed signals and the related
S-transform.
As discussed in the previous section, the method proposed by
Ponzo et al. (2010) was based also on the frequency evaluation
before, during and after an earthquake. In the method, a partially
solved problem is the possibility to automatic evaluation of the
fundamental frequency changes during the strong motion phase. Here
a new approach for the automatic evaluation of the fundamental
frequency over it is be shown. The fundamental frequency is
constant for linear structural behavior. It can get lowered only by
non-linear behavior. Hence, an upper bound for the frequency domain
is maintained.
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When the frequency domain upper bound is established, just using
ambient noise recordings, it is possible to use a limited domain
for the interferometric analyses and for the S-Transform of the
IRFs evaluated at the top level (using the windowed signal). At
this purpose it is important to decide how is the length of the
selected moving time-window and the related overlap. Generally, the
time-window length is fixed as a function of the fundamental period
of the structure:
w 10 T
where w (in seconds) is the moving time-window length and T is
the fundamental period of the monitored structure. With regards to
the moving time-window overlap, basing on the results obtained in
this work, a good rule seems to be 50% of the considered
time-window length.
In the following it is possible to find an example of
application of the proposed procedure to automatic evaluate the
fundamental frequency variation of the structure before, after and
during the earthquake. The elastic starting frequency of the
structure, as mentioned before, is equal to 2.0Hz with a period
equal to 0.5sec. Using the rule established before it is necessary
to use a moving time-window length greater than 5sec. It is worth
noting that during the earthquake, if the structure exhibits a
nonlinear
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Figure 11 Nonlinear Interferometric Analysis performed on the
top floor accelerometric recording and S-Transform evaluated on the
single IRF
It is worth noting from Figure 11 that the instantaneous
fundamental frequency of the structure changes over time starting
from a value equal to 2.0Hz, reaching a minimum frequency equal to
0.85Hz and concluding with a fundamental frequency equal to 1.10Hz.
For each time-window the fundamental frequency can be automatically
evaluated considering the S-Transform results. In fact, the
fundamental frequency corresponds, for each time-window, to the
frequency related to the maximum value of the S-Transform for.
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3) H. Inada et al. Improvement on Health Monitoring System Using
Self-diagnosis Materials for Practical Application
Introduction Programming of sensors to memorise peak values of
deformation of strain caused by the catastrophic disasters of
earthquakes and other such calamities in Structural Health
Monitoring is essentially required. Also, the ability of the
sensors to retain this experienced information makes it possible to
remove the need for continuous monitoring of the structures. In
previous studies, the conductive fiber reinforced composite, the
glass fiber reinforced plastics containing carbon black particles,
has been confirmed to respond sensitively against applied strain
and memorise the peak value. Because the percolation structure
formed by carbon black causes irreversible change in resistance,
the sensor maintains the electrical resistance value corresponding
to the applied peak strain. Research has been conducted by the
authors of the paper using the carbon materials as electrical
conductive sensors. Earlier, conductive fibre reinforced
composites, glass fibre reinforced concrete containing carbon black
particles were tested and had responded positively. Due to the
percolation structure formed by the carbon black, it leads to an
irreversible change in the resistance. Hence, it can memorise the
changed resistance values and hence the deformation can be
extracted from the value of the changed resistance. These have been
used on reinforced concrete structures with positive results.
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Self-Diagnosis Materials and their Production
Characteristics of materials The schematic drawing of
self-diagnosis materials is shown in Figure 12. The rod-shaped FRP
is composed of glass fibers and matrix phase consisting of
thermoset epoxy. High-structure carbon black is dispersed into
resin as conductive particles. The particle volume fraction is
typically set at 5.8%. After being cured at 160C for 90min, the
materials are carbonized through a pyrolysis process at 500C in N2
ambient. The carbonized composites were found to acquire high
sensitivity and distinguished ability to memorize peak strain. The
composite is utilized as a sensor by attaching grips and electrodes
at both ends. In this study, two sizes of sensors as shown in
Figure 13 are applied. As shown in the figure, two pairs of cables,
connecting to outer current electrodes and inner voltage
electrodes, are attached.
Figure 12 Schematic drawing of materials.
Figure 13 Two types of sensors.
Tensile tests were conducted on the sensors in order to evaluate
its performance as strain sensors. A relation between the applied
strain and the electrical resistance of the sensor was deduced as
follows:
= ab ; a = 6.86103 , b = 0.384
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The resistance is represented as = (RR0) / R0 where R0 is the
initial resistance value. The values of a and b were found by
regression of the observed data.
The example of results obtained by tensile test and estimated
relation by the above equation is shown in figure 14. As shown in
the figure, sensor shows a distinguished memory function, but has
lower detectivity against small strain under 1000.
Figure 14 Result of tensile test and regression
Development of production method of materials
Due to the non-uniformity of the cross sectional shape and
dispersion of carbon black in the resin, there is variability in
the tensile tests performed on the materials. Initially, the
materials were manufactured one at a time, thus leading to the
variation in the properties. In later stages, the materials are
made in continuous batches to maintain equal quality throughout.
Also by continuous manufacturing, the production costs also reduce.
The production system using pultrusion process has been employed,
with the mass production line as shown in the figure. Carbon black
is dispersed into resin and warmed well in temperature controlled
bath of 80C in order to accelerate uniform impregnation into glass
fiber. The composites are gradually shaped into rod and cured at
the same time, by passing them through a heated die with inner
Teflon-coating. As a result, two rod shaped materials with
different diameters for two types of sensors are formed
simultaneously as shown. The diameters of each composite are 1.5mm
and 0.9mm, respectively.
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Figure 15 Manufacturing Process
Displacement measuring device using the Self-Diagnosis
Materials
Development of device
The developed measuring device is as shown in Figure 16. The
device is composed of small sized sensor with helical compression
spring, aluminum cylinder and tensile rod, which are all set
coaxially. The rod and the cylinder are connected to two fix
points, and slides from side to side smoothly with each other. The
sensor is installed in the cylinder for protection and made
watertight. Both ends of the sensor are clamped to cylinder and rod
via spring, and relative
displacement caused between two fix positions are distributed to
sensor (SE) and spring (SP) allocated according to their stiffness.
Setting the gauge length and stiffness of the sensor LSE and EASE,
the constant of spring kSP, relation between displacement X and
strain of sensor SE is represented as follows: X = (EASE + LSE kSP
) . SE kSP
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Therefore, sensitivity and allowable displacement of the device
can be controlled by specification of spring. Experimental results
shown below have been obtained by specifying allowable displacement
as 10cm using spring with constant of 28.3N/mm.
Figure 16 Displacement of measuring device
.
Vibration tests of displacement measuring device
Vibration tests were conducted on shake tables where only
one-directional excitation was applied. The aim was to evaluate the
applicability and performance of the displacement measuring
devices. Because the peak memory sensor is applied to static
post-event measurement, its performance is generally evaluated
through static tests. However, in the real world scenario, sensors
are required to record the response of the target structures
against dynamic loading such as earthquakes.
Figure 17 gives the outline of shake table experiments
conducted. One fixed part of device is mounted on the fixed table
with the other end on the shaking table, vibrating in the
horizontal direction (unidirectional vibration). The frequencies
used in the sine sweep were 1, 2 and 5 Hz with amplitudes of 10, 10
and 5 cm respectively. After oscillating in constant amplitude
during certain period, amplitude is decreased gradually. Number of
specimens for each frequency is three. Relative displacement of
shaking table is measured by laser displacement meter for
comparison.
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Structural Health Monitoring of Concrete Structures
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Figure 17 Measurement of Displacement during Shake-Table
Tests
Figure 18 Examples of test results.
The examples of obtained time waveforms of displacement and
electrical resistance of the sensors are shown in Figure 18.
Variation of resistance is represented as above-mentioned variation
ratio . In all test condition, the sensor increases its resistance
value only against the displacement in tensile direction, and keeps
peak value corresponding to the maximum displacement. Therefore,
the sensor is confirmed to show the expected performance as peak
memory measuring device even against dynamic loading. The sensor
also shows apparent resistance variation up to the maximum
frequency of 5Hz in the tests, which demonstrates enough capability
to follow the response of general structures against external
excitations such as earthquakes. In the result of higher frequency,
slight phase delay in response of the sensor against displacement
has been observed. The mechanism and the effect on the sensing
accuracy are being investigated in foregoing studies.
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Structural Health Monitoring of Concrete Structures
25
Chapter 3
Conclusions Structural health monitoring including the dynamic
identification techniques is getting a firm foothold in both the
scientific as well as the civil community. The need for the
assessment of the aged and important structures health has been on
the rise for better longetivity of the structures and better
serviceability. Various methods are being developed to assess the
various properties of the structures which include concrete
structures predominantly, given that most structures in the world
are made of reinforced concrete.
The meaning of structural health monitoring, its needs and its
objective were discussed initially. The various processed of
structural health monitoring were mentioned along a few newly
developed methods of assessing the health of a structure after an
earthquake and similar phenomena were henceforth discussed.
In the first case study, a piezoelectric-based sensor system was
described and developed for the structural health monitoring of
concrete columns under seismic loadings. From the experimental
results of the shake table test and the in situ cyclic loading
test, the DI (damage index) obtained which were proposed for the
specific sensing mechanism increased as the damage levels
increased. Also the drift ratio of the columns had a similar
variation as the damage indices. The proposed process of health
monitoring can be used to predict the health of the structure and
the level of damage after earthquakes and similar calamities.
In the second case study discussed, it was seen that techniques
based on Fourier transform provide good results when the response
of the system is stationary, but fail when the system exhibits a
non-stationary, time-varying behaviour. In 1996, Stockwell
introduced a new powerful tool for the signals analysis: the
S-Transform. Compared with the classical techniques for the
time-frequency analysis, this transformation shows a much better
resolution and also offers a range of fundamental properties such
as linearity and invertibility. The ability to investigate the
non-stationary response of structures opens new scenarios, giving
the opportunity
to explore new possibilities. The paper dealt with the
combination of this S-transform and
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Structural Health Monitoring of Concrete Structures
26
Interferometric Approaches in determining the change in the
fundamental frequencies of the structure and the displacement from
column to column on different stories.
In the third case study described, newly developed damage
detection devices using self-diagnosis materials have been
described. Their usage for damage detection for various structures
is discussed. However, it had shown variation in quality due to
absence of mass production lines. By the advent of pultrusion
methods, the variational aspect of the sensors has been
significantly reduced. The memory aspect of the sensors helps in
the retention of the data after certain catastrophic events like
earthquakes.
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Structural Health Monitoring of Concrete Structures
27
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