The Use of a Neural Network in Nondestructive Testing
by Donald G. Pratt, Mary Sansalone and Jeannette Lawrence
April 25, 1990
Nondestructive testing (NDT) methods are techniques used to obtain
information about the properties or the internal condition of an object
without damaging the object. Thus NDT methods are extremely valuable in
assessing the condition of structures, such as bridges, buildings, and
highways. Because of the current emphasis on rehabilitation and
renovation of structures, there is a critical need for the development
of NDT methods that can be used to evaluate the condition of structures
so that effective repair procedures can be undertaken.
Typically, NDT methods are used to obtain information about a structure
in an indirect way. For example, by measuring the speed of stress
(sound) waves as they travel through an object and studying how the
waves are reflected within the object, one can determine whether or not
flaws exist within the object.
Of particular interest to structural engineers is the development of
NDT techniques for evaluating reinforced concrete structures.
Currently, the practical techniques that can detect cracks in concrete
use acoustic impact, infrared thermography, and ground penetrating
radar. However, none of these methods possesses all the desired
qualities of a crack detection system [1,2], which are reliability under
various site conditions, capability for rapid testing of large areas,
and ease of use.
Recently, a new nondestructive testing technique has been developed for
finding cracks in concrete structures. This method was developed at the
National Institute of Standards and Technology (NIST, formerly National
Bureau of Standards) by Carino and Sansalone and is called Impact-Echo
[3]. Ongoing research programs at both NIST and Cornell University are
aimed at developing the theoretical basis and practical applications for
this new technique. One project carried out at Cornell University has
developed an automated impact-echo test system in the lab which will be
adapted for field use. Key aspects of this project are the development
of hardware and software for a field system. The goal is to develop a
field test system that is reliable, rapid, and relatively simple to use.
OVERVIEW
This article presents a new method for automating and simplifying
impact-echo signal analysis and data presentation with an artificial
intelligence technique that uses a brain-like neural network. We begin
with a brief introduction to the impact-echo method. Next, the
application of the neural network to the analysis of impact-echo data
obtained from concrete plates containing voids is discussed. Two neural
network design approaches are reviewed and a discussion of neural
network effectiveness is included in the final section.
THE IMPACT-ECHO METHOD
In impact-echo testing, a stress pulse is introduced into the concrete
by mechanical impact. Hardened steel spheres are used to strike the
surface, which produces an impact duration of 20 to 80 microseconds,
depending on the diameter of the sphere. Such an impact generates a
pulse made up of lower frequency waves (generally less than about 50
kHz) that can penetrate into a heterogeneous material such as concrete.
The pulse propagates into the concrete and is reflected by cracks and
voids and the boundaries of the structure. A transducer that measures
displacements at the surface caused by the reflected waves is placed
next to the impact point.
The recorded surface displacement waveforms can be analyzed to find the
depth to a reflecting surface, such as the bottom surface of the plate
or an internal crack. For example, in a solid plate the pulse generated
by the impact is multiply reflected between the top and bottom surfaces
of the plate setting up a transient resonance condition. Each time the
pulse arrives at the top surface it produces a characteristic downward
displacement. Thus the waveform is periodic. The round-trip travel
path for the pulse is approximately equal to twice the thickness of the
plate (2T), and the period is equal to the travel path divided by the
wavespeed (C). Since frequency is the inverse of the period, the
dominant frequency, f, in the displacement waveform is:
f = C / 2T (1)
The frequency content of a digitally recorded waveform is obtained using
the fast Fourier transform (FFT) technique [3,4]. In the amplitude
spectrum obtained from the FFT of the waveform] there is a single large
amplitude peak at the frequency corresponding to multiple reflections of
the pulse between the top and bottom plate surfaces. The frequency
value of this peak, which is called the thickness frequency, and the
wavespeed in the plate can be used to calculate the thickness of the
plate (or the depth of an internal crack if reflections occur from such
an internal defect) using Equation (1) rewritten in the following form:
T = C / 2f (2)
For a wavespeed of 3450 m/s and a peak frequency value of 3.42 kHz, the
calculated thickness of the plate is 0.5 m, which agrees with the actual
plate thickness is 0.5 m.1
For a given concrete specimen, wavespeed is essentially constant and so
Equation (2) relates the frequency of a point on the amplitude spectrum
to the depth of a reflecting surface within the specimen. This
relationship can be used to convert the horizontal axis of the amplitude
spectrum from frequency to depth. In addition, the spectra can be made
non-dimensional for a structure of constant thickness if the horizontal
axis is expressed as a percentage of the thickness. The resulting graph
is called the reflection spectrum. In one example a frequency peak at
3.42 kHz appears as a peak at a depth of 100%, indicating reflection
from the bottom of the plate.
In another example, a reflection spectrum obtained from an impact-echo
test on a 0.4 m thick plate containing a 0.4 m diameter void located 0.3
m below the top surface of the plate. Reflection from the void produces
a dominant peak at about 75% of the plate thickness.
In the impact-echo method, tests are carried out at selected points on
the structure, the location of which depends on the geometry of the
structure and the type and size of flaw one is trying to locate. In a
typical filed application, tests would be carried out at many individual
points. Automating the interpretation of reflection spectra is
necessary for a rapid and easy to use field test system. We used an
artificial neural network as a way of training the computer to recognize
the key features of reflection spectra.
INTERPRETING IMPACT-ECHO DATA
A commercial neural network simulation package called BrainMaker,
produced by California Scientific Software, was chosen to interpret the
results of impact-echo tests. This product allows the user to adjust
the various network parameters, such as the number of neurons in each
layer, the format of the inputs and outputs, the neuron transfer
function, etc. The software has a proprietary back propagation
algorithm that uses integer math and runs at 500,000 connections per
second. Creating and training a network is done in a graphical
interface, with pull-down menus and dialog boxes for use with the keypad
or a mouse. The program is very easy to use and comes with extensive
documentation that provides an excellent introduction to neural
networks, both in theory and application.
Reflection spectra are the inputs to the neural network. In the first
design approach, two outputs were used which represented 1) the
probability of a flaw and 2) the depth of the flaw. This design proved
too difficult; an analysis is presented in the next section. The final
network design used 11 output neurons: one is the probability that a
flaw exists and ten others are for the approximate depth of the flaw.
The ten depth outputs give the flaw depth within each 10% increment of
the structure’s thickness.
The absence of a flaw shows up on a reflection spectrum as a single peak
at 100% of the structure thickness, and so a flaw probability of 0% is
associated with a flaw depth of 100%. A reflection spectrum and the
corresponding network output for a solid 0.4 m thick slab shows a low
flaw probability and a high probability at 100% of the slab’s thickness.
A reflection spectrum and neural network output obtained from a test on
a 0.4 m thick slab containing a 0.2 m void at a depth of 0.2 m shows a
high flaw probability coupled with a high probability at 50%, indicating
a flaw between 40% and 50% of the thickness of the slab. Thus the
network is capable of detecting the presence of a flaw and resolving the
flaw depth to within 10% of the thickness of the structure.
In order for the network to learn to interpret reflection spectra
correctly, the training set must include a wide range of flaw
conditions. Each member of the training set includes the reflection
spectrum obtained at a particular test point and the target output for
this point. The target output is the flaw probability and the depth of
the flaw, both of which must be accurately known. Some of this data is
acquired from impact-echo tests on laboratory specimens containing
simulated voids. However, it is impractical to construct laboratory
specimens for every case one would like to use in training a network.
So, the results obtained from numerical simulations of impact-echo tests
on structures containing voids [5] are also used. Numerical simulations
provide a fast and inexpensive way to generate a variety of data for the
training set, compared with using laboratory specimens. The network
used in the examples described above was trained with data from
laboratory specimens and numerical simulations.
The system used to do impact-echo testing in the laboratory includes
data acquisition hardware with 12-bit resolution installed in a portable
80386-based computer operating at 25Mhz. The displacement transducer
uses a small conical piezoelectric element attached to a large brass
backing. This transducer has a broadband output that provides a very
faithful response to displacement. The sensitivity is on the order of 2
X 10^8 volts per meter. Stress pulses are introduced into the structure
using mechanical impact, either by dropping hardened steel spheres or
using a spring-loaded impactor.
The sampling and triggering parameters for the data acquisition card are
under software control, and are set so that the data is taken
automatically when an impact is produced. All the signal analysis is
done in software, including the FFT amplitude spectrum computation and
the neural network simulation. These two algorithms account for the
majority of the processing time. A supervisory program is being
developed with the capacity to gather test data for training new
networks, run tests using previously trained networks, and display the
reflection spectrum and network output. At the present stage of
development, a single test takes about two seconds from the time the
impact is produced to the point at which the output is displayed on the
screen.
THE NEURAL NETWORK DESIGN
This application was designed using the BrainMaker simulator from
California Scientific Software. The training algorithm is the
back propagation algorithm and the sigmoid transfer function is
selected. The learning rate, which controls the amount adjustment to
the weights, is set to a nominal value of 1 (0 prevents training; 4 is
the absolute maximum). The training tolerance, which specifies how
close the output must be to the training pattern to be considered
correct, is set to 0.1 (90% accuracy within the possible output range).
Three layers are used. The first layer is the input layer which reads
in the data to be analyzed. The second or “hidden” layer processes the
information from the first layer and sends it to the third, or output
layer, which produces the result.
In order to use a back propagation network, a training file is needed
which consists of sets of input and output pairs. Each pair of input
data and known output results is called a fact. This application’s
training file consists of 59 facts. Each fact has 150 inputs and 11
outputs, hence there are 150 input neurons and 11 output neurons.
Each input neuron is assigned a vertical slice of the reflection
spectrum. The value presented to each input neuron represents the
amplitude at a particular frequency range which is 1/150 of the
waveform’s total frequency range. One of the 11 outputs correspond to
the probability or certainty of a flaw, and 10 others the range of flaw
depth. For training the appropriate flaw depth is set to 1 with all the
others set to 0. The appropriate flaw depth is the known state of the
test specimen.
To train the network, the program presents the facts one at time and
computes the actual network output for that fact. The actual output is
compared to the known result and the difference is used to make
adjustments to the network connections. Facts for which the network’s
output is not within the training tolerance are considered bad, and
statistics are displayed as such on the screen. The inputs, outputs,
and hiddens can be displayed as numbers, symbols, pictures or
thermometers. While training, the network is shown all of the facts,
over and over until it learns everything to the performance level
specified.
The first design used only two output neurons: one for the probability
of a flaw and the other represented the depth of the flaw directly by
its numeric output value. Although this network trained quickly (86
runs in 15 minutes on a 25 MHz 386), it did not test well. It was
observed that the output was sensitive to the amplitude of the inputs
rather than the features. It did not pass the test on laboratory
samples within the required accuracy. Upon consideration, it was
thought that the network was experiencing difficulty in the way a person
might. Imagine trying to judge the exact length of lines on a wall from
quite a distance away with nothing to compare them to. This is a
difficult task. But if asked what the relative length of two lines is
(e.g., Is the first line half the length of the second?), it becomes an
easy task. This concept sparked an idea for a new design. The new
design allowed the neural network to answer “yes” or “no” to questions
like “Is there a flaw at a depth of 10 – 20%?”, rather than ask it to
come up with a precise number.
The second design used 11 output neurons instead of 2. By adding more
output neurons which represent the flaw depth in increments, it is
easier for the network to train. With multiple outputs (each of which
represents the probability of a flaw existing within a particular range
of the total depth), the network picks one of many instead of using one
neuron to indicate the depth directly. Distributing the output has also
been found by California Scientific Software to be a good design
technique. This scheme also permits the detection situations where the
network is unable to make an accurate classification after it’s trained.
In some cases, the output conditions may not make sense. For example,
when the network says that the flaw depth may be at 10% AND it may be at
50% (which is indicated by both neurons being partially turned on), it
means the network is having trouble interpreting the input. If the
first network were to encounter such an ambiguous case, the single
output would indicate some depth and it would be hard to interpret the
difficulty it was having.
Still, after increasing the number of output neurons, the network had
difficulty passing the test on laboratory samples. After training,
histogram diagrams were examined. The histogram shows that the neuron
connections are tending to bunch up toward the negative end of the
weight values. This is often a bad sign that the network is making
major changes to the weights without being effective (the number correct
is only 47 out of 54 at this point). Sometimes a network eventually
trains and tests out well when this happens, but this one did not. It
was found that 10 hidden layer neurons was too few.
The problem was alleviated by increasing the number of hidden neurons to
20. It had taken 169 iterations to train but now with 20 hidden neurons
the new network trained in 72 iterations, and it got all of the testing
facts correct.
ADVANTAGES OF THE NEURAL NETWORK
The ability of the neural network to learn the key features of input
patterns makes it a useful tool for interpreting impact-echo reflection
spectra. The relative ease with which a network can be defined,
trained, and used makes the technique attractive for developmental work
where the system is likely to undergo many revisions before a final
system is produced. Once the design change to 11 outputs was conceived,
implementation was accomplished in a few hours.
The network output is a set of probabilities that provides a simple way
to measure the certainty of the result. For example, if the flaw
probability is 55%, the network is suggesting uncertainty in the data,
compared with an output of 98%, which shows close correlation with
members of the training set.
The neural network provides an automated method of determining flaws in
concrete without destroying the structure. Testing of the neural
network revealed a success rate of about 90% with laboratory concrete
samples. Success is difficult to precisely determine for several
reasons. One difficulty occurs when the sensor is placed near the edge
of a flaw. The network output may be vague or confusing. The edge of a
flaw can cause reflections from many levels in the concrete. In this
case, the network output could be taken in the context of the results of
tests of nearby areas to determine that it was in fact an edge which
caused the confusing output. This decision could be automated by
another neural network which looked at the results of several tested
proximal areas at once.
Other approaches for finding flaws range from the drilling of core
samples to the use of radar. The first method is destructive,
time-consuming and only permits checking a small percentage of the area.
The second require expensive equipment and isn’t effective when there’s
steel reinforcement. These approaches experience the same problem when
the sensor is not placed directly over the flaw. They also have other
problems of not being capable of rapidly testing large areas, reliable
under various site conditions or easy to use. A neural network is
better because it uses a non-destructive technique, the system can be
built from off-the-shelf parts, its speed enables quicker interpretation
of results, its flexibility lends it to use as a developmental tool, and
the results will be consistent.
CONCLUSION
A new method for automatic interpretation of nondestructive test data
has been presented. The use of an artificial neural network provided a
quick and accurate means of interpreting the results of impact-echo
tests obtained from concrete structures.
On-going work is focusing on developing a rugged field test instrument
based on the impact-echo laboratory test system. When this objective is
realized, a tool will be available for rapid and reliable detection of
cracks in concrete structures.
To date, the impact-echo testing technique has been used in trail field
studies for detecting voids in a concrete ice-skating rink [6] and in
reinforced concrete slabs [7]. Once a rapid field instrument is
developed, the method can be used routinely for nondestructive testing
of plate-like structures such as slabs, pavements and walls. For these
applications, it is expected that a neural network will be used to
automate signal processing.
A Canadian mining company is currently negotiating with Cornell
University for a system that will help them determine if the structure
of a decommissioned mine is safe enough to recommission the mine.
Acknowledgements:
Research sponsored by grants from the Strategic Highway Research
Program, Project C-204 and from the National Science Foundation (PYI
Award).
BrainMaker neural network simulation software ($195) was provided by
California Scientific Software, 10141 Evening Star Drive #6, Grass
Valley, CA 95945-9051. (916) 477-7481.
——————–
Footnotes:
1. The frequency resolution in the amplitude spectrum and thus the
accuracy of plate thickness or crack depth predictions will depend on
the sampling rate and duration of the recorded waveform.
References:
1. Manning, D.G. and Holt, F.B., “Detecting Deterioration in
Asphalt-Covered Bridge Decks,” Transportation Research Record 899, 1983,
pp. 10-20.
2. Knorr, R.E., Buba, J.M., and Kogut, G.P., “Bridge Rehabilitation
Programming by Using Infrared Techniques,” Transportation Research
Record 899, 1983, pp. 32-34.
3. Sansalone, M. and Carino, N.J., “Impact-Echo: A Method for Flaw
Detection in Concrete Using Transient Stress Waves,” NBSIR 86-3452, NTIS
PB #87-104444/AS, Springfield, Virginia, September, 1986, 222 pp.
4. Carino, N.J., Sansalone, M., and Hsu, N.N., “Flaw Detection in
Concrete by Frequency Analysis of Impact-Echo Waveforms,” in
International Advances in Nondestructive Testing, Vol. 12, ed. W.
McGonnagle, Gordon and Breach Science Publishers, 1986, pp. 117-146.
5. Sansalone, M., and Carino, N.J., “Transient Impact Response of
Plates Containing Flaws,” in Journal of Research of the National Bureau
of Standards, Vol. 92, No. 6, Nov-Dec 1987, pp. 369-381.
6. Sansalone, M., and Carino, N.J., “Laboratory and Field Studies of
the Impact-Echo Method for Flaw Detection in Concrete,” Nondestructive
Testing of Concrete, SP-112, American Concrete Institute, Detroit,
1988, pp. 1-20.
7. Sansalone, M. and Carino, N.J., “Detecting Delaminations in Concrete
Slabs with and without Overlays Using the Impact-Echo Method,” ACI
Materials Journal, V. 85, No. 2, Mar.-Apr. 1989, pp. 175-184.
8. Stanley, J., “Introduction to Neural Networks,” (c) California
Scientific Software, Sierra Madre, California, January, 1989
About the authors:
Donald G. Pratt is a doctoral student in Civil Engineering at Cornell
University. Mary Sansalone received a Ph.D. in structural engineering
from Cornell University, where she is an assistant professor. Prior to
joining the faculty at Cornell, she was a research engineer with the
National Institute of Standards and Technology. Mr. Pratt and Dr.
Sansalone may be reached at Cornell University, Hollister Hall, Ithaca,
NY 14853. Jeannette (Stanley) Lawrence is a technical writer
specializing on the subject of neural networks. She may be reached at
California Scientific Software, Grass Valley, CA.
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