GENETIC FEATURE SELECTION AND STATISTICAL

CLASSIFICATION OF VOIDS IN CONCRETE STRUCTURE

G. Acciani, G. Fornarelli, D. Magarielli and D. Maiullari

Departement of Electrical and Electronical Engineering, Politecnico di Bari, Via E. Orabona 4, 70125 Bari, Italy

Keywords: Ultrasonic waveforms, Non-Destructive Techniques, Concrete Structure, Genetic algorithms, k-NN.

Abstract: In this work simulated ultrasonic waveforms in a concrete specimen obtained by a software based on finite

element method were used to develop an automatic inspection method. A piezoelectric transducer is used to

generate stress waves that are reflected by voids. Then the waves are received by another transducer set at a

fixed distance from the first one on the same specimen surface. Time and frequency features has been

extracted from the waveforms, the most significant features have been chosen by a genetic feature selection

and the classification performances were estimated referring to a k-NN classifier.

1 INTRODUCTION

Concrete structures like bridges, tunnels, walls or

infrastructures require periodic inspections and

quality control to assess their structural integrity. In

order to track and evaluate the symptoms of

deterioration or damage that may compromise

service quality or safety, many advanced Non-

Destructive- Techniques (NDT) have been

developed and applied (Berriman, 2006).

During the last years, the methods based on the

propagation of ultrasonic waveform have attracted

researcher’s interests due to their effectiveness in the

localization of structural components and internal

defects like cracks or voids, particularly the pulse-

echo method is a common, simple and suitable

approach for field operation test. By driving a

dynamic force via a piezo-electric element, elastic

waves are generated and propagated in concrete

where the stress waves are scattered, reflected,

attenuated and resonated. A receiver is used to detect

the arrival of reflected waves (echoes), so that the

time of flight (round–trip travel period) of these

waves can be calculated. Wave reflections are

produced by internal defects, interfaces between

materials with different densities and elastic

properties and boundaries of the solid. This method

is used to determine the location of defects or

interfaces by knowing the velocity of stress waves

and measuring their time of flight (Fan

, 2006). Many

methods was developed in which the evaluation of

the travel time of ultrasonic signal echoes is

focalized on defects of large extension (delamination

or cracks). In this paper an automatic inspection

method based on a genetic algorithm and a statistical

approach is proposed. In this method the travel time

estimation is avoided and the localization of defects

with small dimensions is carried out.

In the field of the automatic classification of

defects the greater problems to be solved are how to

extract significant flaw information and how to

interpret this kind of information. In this work, it has

been referred to 71 time and frequency features

characterising ultrasonic waveforms used for the

non-destructive defect detection in not accessible

pipes (Acciani, 2006). The significant flaw

information is then obtained by the feature selection

which is committed to a genetic approach, based on

the k-Nearest Neighbor (k-NN) classifier.

2 ULTRASONIC PROPAGATION

In this work the considered test is an homogeneous

isotropic concrete specimen whose geometric and

physical properties are summarized in Table 1.

On a free surface of the test specimen, a

transducer consisting of a piezoelectric thin layer

generates the stress waves which are detected from a

receiver situated on the same side of the emitter

(indirect transmission).

231

Acciani G., Fornarelli G., Magarielli D. and Maiullari D. (2008).

GENETIC FEATURE SELECTION AND STATISTICAL CLASSIFICATION OF VOIDS IN CONCRETE STRUCTURE.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 231-234

DOI: 10.5220/0001692802310234

Copyright

c

SciTePress

Table 1: Geometric and physical characteristics of

concrete specimen.

Quantity Value

Width (x

2

) 1.0 m

Depth (x

3

) 0.3 m

Height (x

1

) 1.0 m

Young's Modulus E=25×10

9

Pa

Density ρ=2300 kg/m

3

Poisson's ratio ν=0.33

First Lamé constant μ=9.398496×10

9

Pa

Second Lamé constant λ=1.824414×10

10

Pa

Velocity of longitudinal wave v

P

= 4013.08 m/s

Velocity of transversal wave v

S

= 2021.46 m/s

Velocity of surface wave v

R

= 1884.00 m/s

The piezoelectric layers are Piezo Zirconate

Titanate (PZT 27) commonly employed in ultrasonic

techniques whose diameter is D=0.05 m,

piezoelectric constant d=425×10

-12

m/V, strain-

voltage constant h=1,46×10

9

V/m. The excitation

voltage applied to the piezoelectric layer is an

Hamming windowed sinusoid, with a duration of

three periods, whose analytical expression is given

by the following equation:

() ()

eM

1f 3

Vt V 1 2π t2πft t [V]

23 f

⎡⎤

⎛⎞

⎛⎞ ⎛⎞

=⋅ − ⋅ ⋅≤

⎢⎥

⎜⎟

⎜⎟ ⎜⎟

⎝⎠ ⎝⎠

⎝⎠

⎣⎦

cos sin

(1)

where f is the central frequency excitation and V

M

is

the amplitude of the sinusoid. The ultrasonic waves

propagation has been analyzed on the plane

containing the transducer axis as shown in figure 1.

The propagation in the specimen under test in

absence of body forces can be written as follows

(Rose, 1999):

()()

2

2

2

ρλμ μ

t

∂

=+∇∇⋅+∇

∂

u

uu

(2)

where

T

123

[u u u ]=u

is the displacement field.

Moreover, appropriate boundary conditions must

be added to these equations. At free surfaces it is

requested that the traction vector vanish, thus

boundary conditions are:

⋅=σ n0

(3)

where σ is the stress tensor and

[]

T

123

nnn=n

is the outward unit normal to the boundary.

On each point of the contact surface between the

specimen and the emitter the displacement values

have been imposed by the equation:

Figure 1: Simulation.of Set-up.

()

3e

udVt=⋅

(4)

The displacements on the contact surface

between the specimen and the receiver are

proportional with the voltage across the piezoelectric

transducer through the strain-voltage constant h:

(

)()

r3

Vt hu t=⋅

(5)

The software used to simulate the considered

case is Comsol 3.3, a commercial code based on the

Finite Element Method (FEM). The considered

simulation have been carried out considering an

excitation frequency f=70 kHz, an analysis time

T=4×10

-4

s, a quad mapped mesh with dimension

sides m=0.6 cm. Such value of m corresponds to

1/10 of the central frequency wavelength, in order to

guarantee the numerical accuracy provided by the

solution algorithm.

In figure 2 a) the qualitative progress of the

wavefronts for the specimen previously considered

with two square voids (side dimensions 2 cm) are

depicted. Particularly, the radiation field at 78% is

pointed out by the oblique lines. It means that the

displacement amplitude of a point of fixed

wavefront reduces to the 78% compared with the

displacement amplitude of a point belonging to the

same wavefront and aligned with the transducer

axis. The two considered defects are placed on the

center of the radiation field and on the same

wavefront on the border of the radiation field.

Figures 2 b) and 2 c) represent u

3

displacement

waveforms evaluated on the same position of the

emitter for the central and not central defect,

respectively. It is possible to note that in the second

case the echo amplitude due to the defect could be

undetectable in the presence of noise.

Transducer axis

Receiver

Analysis plane

Concrete specimen

ICEIS 2008 - International Conference on Enterprise Information Systems

232

Figure 2: Comparison between u

3

displacements: a)

defects position; b)waveform for defect aligned with

transducer axis; c) waveform for defect aligned with the

radiation field border.

For these reason the defects considered in the

present work are placed all in the radiation field

previously defined.

3 DIAGNOSIS APPROACH

In this section the proposed approach to classify the

defects on concrete structures is discussed.

A set of 71 parameters characterising ultrasonic

waveforms have been extracted from the simulated

waveforms. The most significant features have been

chosen by a genetic feature selection (Muni, 2006).

GA are a class of robust problem-solving

techniques based on a population of possible

solutions, which evolve through successive

iterations by means of the application of three

genetic operators: selection, crossover and mutation.

A solution is represented by a finite sequence of

0’s and 1’s called chromosome. For the problem of

feature selection, a chromosome has length d that

corresponds to the total number of features. A ‘0’

represents a rejected feature, whereas a ‘1’ stands for

a selected feature.

To start the solution process, the GA has to be

provided with an initial population. The most

commonly used method is the random generation of

initial solution for the population. Then the

chromosomes are allowed to ‘crossover’: two

chromosomes, generally selected with a random

criterion, exchange their parts at a chosen point to

create two new chromosomes. Chromosomes are

also allowed to ‘mutate’: by flipping one or more

bits can be made to a chromosome.

The optimization process is carried out in

‘generations’, each time a population of new

chromosomes is generated, until some criterion is

met. The most commonly used terminating criterion

is the maximum number of generations.

Since the population size is finite, only the ‘best’

chromosomes are allowed to survive. The various

generations experience the processes of crossover

and mutation each of which happens with a certain

probability. The mechanism of crossover interests a

subset of the population that is identified by means

of a criterion of ‘selection’, chosen from these

available in literature. There is also a mechanism of

replication (or ‘elitism’) according to which no

change is made and a certain number of individuals

is simply copied from the current generation to the

next. The next generation is chosen from the new

individuals created and from the previous generation

according to a fitness function that allows to

calculate a fitness score for each of the

chromosomes. In the genetic feature selection, it is

critical to design an appropriate fitness function to

avoid local minima. Traditionally, in the GA-based

feature selection problems, the fitness function used

is simply a hit rate function. The fitness function

considered in this paper takes into account the

classification performance and the ratio between the

used features and the number of all defined features.

In particular, the classification performance has been

estimated referring to k-NN classifier and the

analytical expression of the considered fitness

function is:

()=−

i

i

X

fitness f X

D

α

(6)

where f(.) is the hit rate,

i

X

is the number of

selected features for the chromosome

i

X

, D is the

number of all the measured features, α is a de-

emphasis coefficient that has been fixed equal to

1/10. α coefficient sets the weight of the number of

the features compared with the classification rate.

Some preliminary experiments were carried out

to try out an optimal set of operators and parameters

of the GA useful for the investigation considered in

this work. With the experience gained from these

explorations, the parameters have been set as shown

in the Table 2.

a)

Time 8,5×10

-5

s - u

3

displacement

RADIATION FIELD TO 78% - f = 70 kHz -

D = 5 c

m

b)

u

3

Displacement [m]

Time [ms]

Time [ms]

u

3

Displacement [m]

defect echo

defect echo

c)

GENETIC FEATURE SELECTION AND STATISTICAL CLASSIFICATION OF VOIDS IN CONCRETE

STRUCTURE

233

Table 2: Description of genetic algorithm parameters.

GA parameters settings Value or type

Initial population choice Random

Population size 100

Number of generations 100

Selection criterion Roulette wheel

Elitism (nr. of unchanged

individuals)

10

Crossover (nr. of selected

individuals)

50

Crossover rule Single point crossover

Crossover probability 1

Mutation probability 0.02

Termination criterion Maximum nr of generations

4 EXPERIMENTAL RESULTS

AND DISCUSSION

In order to test the proposed localization method, a

database of 90 simulated ultrasonic waveforms has

been obtained adopting the specimen and the

configuration analysis described in Section 2. In this

test case, 10 classes of defects with different

positions, inside the transducer radiation field, and 9

square voids (side 2 cm) for each class have been

considered, as shown in figure 3. Finally, the

collected signal set has been contaminated and

augmented by additive white Gaussian noise

considering four noisy waveforms for each signal of

the database. The extended data set of 90×4=360

waveforms, has been randomly divided into training

subset (90%) and test subset (10%) with the aim to

examine the robustness of the automatic

interpretation scheme. By according the above

described method, the 71 time and frequency

domain parameters for each waveform of the

database have been evaluated. Then the genetic

feature selection based on k-NN algorithm has been

used to select the optimal feature set for voids

position classification. The performances of k-NN

have been evaluated in terms of Mean Classification

Error (MCE), that is calculated as the mean of the

errors in the classification of the test set waveforms.

The better results have been obtained with the

number of neighbours k = 3 for the k-NN. In this

case, the MCE starts from a value of 38.89% in

correspondence of the whole set of 71 features, but

the GA-based feature reduction allows it to increase

up to the value of 97.22% with a selection of only 7

significant features listed below: Difference between

50% level and 25% level (spectrum CD); Difference

between 75% level and 25% level (spectrum CD);

Difference between 90% level and 25% level

(spectrum CD); Global rise frequency between 25%

level and peak of spectrum; Global rise frequency

between 50% level and peak of spectrum; Global

fall frequency between peak of spectrum and 50%

level; Global rise variance between 25% level and

peak of spectrum (CD = cumulative distribution).

The results show that the adopted method

provides a low error rate for the identification of

position of voids. Therefore, it would be a contribute

to develop an automatic method to localize defects

by means of ultrasonic analysis in concrete

structures. Future works will be devoted to a more

accurate identification of the position of voids.

Figure 3: 10 classes of defect in the radiation field.

REFERENCES

Berriman, J. R., Hutchins, D. A., Neild, A., Gan, T. H.,

Purnell, P., 2006. “The application of time-frequency

analysis to the air-coupled ultrasonic testing of

concrete”, IEEE Transactions on Ultrasonics,

Ferroelectrics and Frequency Control, Vol. 53, pp.

768 – 776.

Fan, X., Zuo, M.J., Wang, X., 2006. “Application of

stationary wavelet transforms to ultrasonic crack

detection”, Canadian Conference on Electrical and

Computer Engineering, pp. 1432 – 1436.

Acciani, G., Brunetti, G., Chiarantoni, E., Fornarelli, G.,

2006, “Automatic Evaluation of Flaws in Pipes by

means of Ultrasonic Waveforms and Neural

Networks”, International Joint Conference on Neural

Networks Sheraton Vancouver Wall Centre Hotel,

Vancouver, BC, Canada, pp. 892 - 898.

Rose, J. L., 1999, Ultrasonic Waves in Solid Media,

Cambridge University Press.

Muni, D.P., Pal, N.R., Das, J., 2006, “Genetic

programming for simultaneous feature selection and

classifier design”, IEEE Transactions on Systems,

Man and Cybernetics, Part B, Vol. 36, pp. 106 – 117.

Square void

Emitter

Receiver

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