FINAL PROJECT (CE3216) Planning Laboratory Tests, Experimental Measurements and Errors SCHOOL OF CIVIL ENGINEERING Dr. DEEPAK T. J.

FINAL PROJECTFINAL PROJECT(CE3216)(CE3216)

Planning Laboratory Tests, Experimental Measurements and Errors

SCHOOL OF CIVIL ENGINEERINGSCHOOL OF CIVIL ENGINEERING

Dr. DEEPAK T. J.Dr. DEEPAK T. J.

Planning a Series of Laboratory Tests

When Carrying out Laboratory or other Practical Investigations, it is important to ensure that:

a) Appropriate Techniques are used

b) Meaningful Results are obtained

For this reason, it is essential that the test programme is designed properly and that care is taken that the correct tests and test procedures are adopted

Ensuring that appropriate techniques are used……

In order to do this, the following questions must be answered:

a) Is the correct equipment being used?

b) Is the equipment accurate enough?

c) Does the equipment have sufficient capacity?

d) Do the tests actually measure what is intended?

Ensuring that Results are meaningful

• Is the instrumentation being read correctly?• Does it make sense to measure the parameter

that is being investigated?• Are the results reproducible?

Adopting a realistic approach to Equipment Design

With particular reference to carrying out a final year project, you will need to:

a) Check what equipment is available

b) If there are costs involved, check that funding is available

c) Modify your plan if necessary

d) Produce a Time Schedule for the investigation

Adopting a realistic approach to Equipment Design

With particular reference to carrying out a final year project, you will need to:

e) Check whether materials are available. Order materials if necessary. Find out when they will be available; allow extra time for delivery in your schedule

f) If you require equipment to be specially made, produce drawings or good sketches including all dimensions, materials and tolerances

Adopting a realistic approach to Equipment Design

With particular reference to carrying out a final year project, you will need to:

g) Then discuss your requirements with the laboratory technicians

h) Check how long it will take for the production of the equipment and whether you can do part or all of the work yourself. The technicians usually have a long order book; be prepared that it may take several months.

An Example – Will the tests do what they are intended to?

• You have been requested to carry out a load test on an individual pile which has been designed to carry a working load of 100 kN

• You have obtained a hydraulic jack with a capacity of 120 kN, with which you intend to apply a load to the pile

• You realise that you need something to jack against and so get the JCB driver to come across and park his excavator (a JCB 3CX) over the pile

• You install the jack between the top of the pile and the underside of the JCB and set up a frame and dial gauge on the head of the pile to measure its settlement

• Satisfied with the set-up, you start jacking to load the pile

How successful do you think your test will be?

Monitoring the effectiveness of the Test Procedure

• In addition to ensuring that the test procedure is correctly designed so that the tests provide the appropriate information, it is also necessary to monitor the results as the test programme proceeds

• Provided that the tests are undertaken with a clear aim in mind and that the test hypothesis is fully understood, it should be possible to judge whether each of the individual test results seems reasonable or whether the test procedure may need revision

Example – Assessing Test Effectiveness

You are testing a 1 metre long concrete plank by clamping both ends and then applying a central load as shown below:

Example – Assessing Test Effectiveness

• Where would you expect the plank to fail?• In each of the first three tests, a failure occurs

10 cm from the left hand end.• Are you concerned? What steps might you take

to adjust your experimental technique?• In this case, the results are such that they would

cause some concern . In this respect, one of the first things that might be considered is to revise the loading configuration.

Example – Assessing Test Effectiveness

• In fact, for a bending test of this type, it is more appropriate to apply two equal loads equidistant from the centre of the test piece in order to apply a uniform maximum moment across a wider distance than for the original loading, which results in a maximum moment at a single point:

Some Practical Problems in Designing Tests

• In addition to the type of problems discussed Previously, there are also some other practical problems that need to be considered:

• For example, you may decide that there are advantages in assessing concrete strength using smaller cubes than the standard 100 mm size

• The advantages here are that you will require less material and the failure loads will be lower, so that you might not require a particularly heavy testing machine

However, can you think of any potential disadvantages you may find???

A Similar Type of Practical Problem

You have identified a research project to look at the behaviour of fill materials to highway structures. The fill in question usually comprises a range of soil particle sizes from 5 mm up to 150 mm. You are going to use a 60 mm square shear box for the tests.•Can you identify some of the limitations that your tests may suffer from?•Can you think of any ways in which these limitations may be overcome?

Checking that the Equipment works correctly

• You must check that all of the equipment is working correctly. This includes:

a) Maintenance – all necessary maintenance must be completed

b) Cleanliness – not least to prevent errors due to contamination

c) Calibration – should be up to date for all relevant equipment

• Double check all readings.

Time Scheduling – allowing enough time for the Tests

Again, this is a particularly important consideration for students’ own final year projects•Before you even start your project you will need to produce a time schedule that takes account of all the tasks you are going to carry out•The Golden Rule is that investigations will almost certainly take longer than you expect.•You must ensure that you allow enough time for all of the following:

Time Scheduling – allowing enough time for the Tests

a) Time to plan and make decisions on what you are going to investigate and how you are going to do it

b) Lead time for preparing, calibrating and checking equipment, delivery and preparation of materials

c) Time to carry out trial runs

d) Time to analyse results from trial runs

e) Time to review and if necessary repeat trial runs

Time Scheduling – allowing enough time for the Tests

Again, this is a particularly important consideration for students’ own final year projects•Time to carry out the actual practical investigation•Time to analyse results•Time to repeat some or all of the tests if necessary•You must allow for any waiting time. For example, oven drying of soil takes 24 hours, concrete cube curing takes 28 days.

Using External Data

Again, considering a case primarily relevant to a final year project, consider the following:

Your employer on your industrial placement is due to carry out a full scale loading test on a structural element comprising part of a reinforced concrete bridge in three months time. She offers you the chance to analyse some of the results and you think that this would make a potentially interesting final year project.

Using External Data

• Do you think it would be a good idea to undertake such a project?

• What potential problems do you think may occur and can you do anything to avoid them?

Potential problems when using External Data

Repeatability of Results

• Repeatability is one of the most important factors in any laboratory test programme

• Individual tests should produce repeatable results; you may need to check this out by:a) Repeating tests when you are carrying out your preliminary

testing programme

b) Designing your final test programme so that you should be able to check the results of certain tests against each other

• You MUST provide enough information in your final report so that other researchers can repeat your experiments in order to verify your findings

Taking Experimental Measurements (A Discussion of Errors)

• All experimental measurements that are taken will be subject to error of some sort

• In order to judge how useful the test data is, it is necessary to estimate how big (or small) these errors are likely to be

• In addition, if the sources of error can be identified, it may be possible to revise the design of the experiment in order to reduce their effects

Reporting Measured Values

• Ideally, when any value is measured, it should be reported in terms of both the value and the expected error

• Hence, values will be reported as:

Best Estimate ± Uncertainty(This will be discussed in more detail later on)

Types of Error

• In simple terms, errors may be broken down into two distinct types:

a) Random Errors

b) Systematic Errors

Random Errors

The main features of random errors are that:

a) They occur in a random fashion and change with each measurement

b) As a result, it is possible to reveal the presence of random errors by repeating measurements

Causes of Random Errors

• Lack of Sensitivity in the Equipment or the observer (this is the most common cause)

• Background “Noise” to the measurement.– For example, natural variations in temperature or

atmospheric pressure

• Failure to define the quantity being measured sufficiently accurately

• Some physical processes are inherently random (e.g. radioactivity)

Dealing with Random Errors

• Random errors are unavoidable – they CANNOT be completely eliminated, although they may be reduced through good experimental design

• The effects of random errors can generally be accounted for by repeating the tests and analysing the results statistically

Systematic Errors

• These are errors which are uniform and reproducible and occur for every measurement which is made

• The effect of systematic errors is to cause a shift away from the true value being measured, as opposed to random errors, which lead to a wider range of readings either side of the true value

Causes of Systematic Errors

• Incorrect calibration of measuring instruments (this will often lead to a “zero error”)

• Faulty Equipment• Incorrect use of the equipment• Failure to account for a relevant effect, e.g. as a

result of making poor or incorrect assumptions about the way in which the experiments work

Dealing with Systematic Errors

These are more difficult to deal with than random errors because they will NOT be identified by repeat testing. Consequently:

a) The major difficulties where systematic errors occur are in identifying that they are present

b) Once a systematic error has been identified, the test procedure should be changed to eradicate the source of the error

c) All test equipment should be properly maintained and should be calibrated regularly

Taking Experimental Measurements

• All experimental procedures will require measurements to be taken

• When any quantity is measured, it is important to understand that the measurement will never be EXACTLY the same as the quantity being measured

• Consequently, we need to have some idea of why the measurement is different to the reality and also what implications this is likely to have

Accuracy and Precision

• These are two important terms which have clear technical meanings

• Accuracy is NOT the same as Precision, so that a measurement can be Precise, but not Accurate and vice versa

A Definition of Accuracy

A measurement is said to be Accurate when it is similar to the true value of the quantity

being measured

A Definition of Precision

A measurement is said to be Precise when a number of repeated measurements of the

same quantity are similar to each other

An example of a measurement being Precise but not Accurate

Say, for example, that I decide to measure the height of one of my students using a 3 metre tape•I do this five times and my measurements are:

– 1.35m, 1.34m, 1.35m, 1.35m, 1.36m•I could then consider a value of 1.35m to represent a precise measurement of the student’s height•However, having finished the measurement, I find that the first 0.5 m of the tape has broken off. This means that the answer I have is not accurate – the student’s actual height will be around 1.85 m and my precise measurement is not accurate

An example of a measurement being Accurate but not Precise

Having failed in my initial measurement of the student’s height, I decide to use another tape measure to repeat the process. However, I can only read this one to the nearest 0.05m

•My readings this time are:

– 1.90 m, 1.80m, 1.85m, 1.75m, 1.95m

•This gives an average value of 1.85m

•Provided that the student actually is 1.85 m tall, I now have an Accurate value, even though my measurements here are less Precise than the first set

Assessing the Accuracy of a given measurement

One way of assessing the accuracy of a given measurement is in terms of the percent of absolute uncertainty of an individual measurement , which is defined as:

Where M is a specific measurement that has been made and Ms is the actual value of the quantity being measured

•The limitation here is that you need to know the value of the quantity being measured.

100% x M

MMty UncertainAbsolute %

s

s

Assessing the Precision of a series of measurements

In order to access the precision with which measurements are carried out, it will be necessary to carry out a series of measurements to determine the percent relative uncertainty, which is defined as:

Where M is a specific measurement that has been made and Mavg is the average value of a series of measurements

•The above equation explains why it is necessary to carry out repeat tests to check whether the test method is sufficiently precise for the purposes

100% x M

MMty UncertainRelative %

avg

avg

Quoting the expected level of Error

• As noted above, all experimental reports should give an indication of the expected error in the measurements being taken

• This will often be discussed in an individual section of the laboratory or research report, although it will also be necessary to quote values of measurements which will include an assessment of the expected

Reporting Measured Values

These should generally be reported to an appropriate number of significant figures based on:•What is being measured•The level of error in the measurement

The second of these factors may require you to assess the overall error in terms of:•The accuracy of the equipment being used•The precision to which you can take measurements

Quoting Results and Errors

Where expected errors are quoted, the results should be quoted to the same level of accuracy as the errors•For example, if a person’s height is measured as 1.87 m with a tape where the expected error is ± 0.01m, then this would be expressed as:

Height = 1.87 ± 0.01 m•However, if the expected error in measurement is ± 0.05 m and the measured height is 1.87 m, it is not appropriate to quote the value to the nearest 0.01 m, so the height would be expressed to the nearest 0.05 m as:

Height = 1.85 ± 0.05 m

Levels of Precision and Civil Engineering Design

• One of the facets of civil engineering design that students find difficult to understand is that engineers can tend to “arbitrarily” round off values in calculations

• The reason for this is that in many instances, whereas high precision is encouraged in many areas of science, this is not necessarily the case in civil engineering

Levels of Precision and Civil Engineering Design

• For example, an engineer using an I beam for a design may have the choice between using two beams of different consecutive sizes, for which:

Ixx = 408,000 cm4 and Ixx = 481,300 cm4

• In such a case, it is not necessary to find a very precise value for the section required – a value to the nearest 1,000 cm4 will much more than suffice. In addition, it is also rare that a choice such as this will be made on the basis of a single value such as this

Levels of Precision and Civil Engineering Design

• This factor should be taken into account when designing laboratory tests, although it is also important (particularly in the absence of design experience) to follow the general rule of calculating individual values as precisely as possible and only rounding up once the final value has been evaluated