textfiles/programming/AI/pratt.txt

              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.