KSCE Journal of Civil Engineering
Vol. 10, No. 1 / Janualy 2006
pp. 33~39
Structural Engineering
Vol. 10, No. 1 / January 2006 − 33 −
Active Sensing-based Real-time Nondestructive Evaluations for
Steel Bridge Members
By Seunghee Park*, Chung-Bang Yun**, and Yongrae Roh*
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Abstract
This paper presents an experimental study on the applicability of piezoelectric lead-zirconate-titanate(PZT)-based active sensingtechniques for nondestructive evaluation (NDE) of steel bridge members. PZT patches offer special features suitable for real-time in-situ health monitoring systems for civil structures, because they are small, light, cheap, and useful as built-in sensor systems. In thisstudy, the impedance-based damage detection method and the Lamb wave-based damage detection method were applied to steelbridge members. Several damage sensitive features were selected: i.e., root mean square (RMS) changes in the impedance andwavelet coefficients, and the time of flights. Firstly, PZT patches were used in conjunction with the impedance and Lamb waves todetect the presence and growth of artificial cracks on a 1/8 scale model for a vertical truss member of Seongsu Bridge, Seoul, Korea,which caused the collapse in 1994. RMS changes in the impedances and wavelet coefficients are found to increase proportionally tothe crack length. Secondly, two PZT patches were used to detect damages on a bolted joint steel plate, which were simulated by loosebolts. The time of flight and wavelet coefficient obtained from the Lamb wave signals were used. The correlation of the Lamb wavetransmission data with the loose bolts was investigated. And, the support vector machine was used for damage classification. Resultsfrom the experiments showed the validity of the proposed methods.
Keywords: real-time nondestructive evaluation, active sensing, PZT, impedance, Lamb waves, steel bridge member, support vector
machines
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1. Introduction
In recent years, structural health monitoring (SHM) is
increasingly being evaluated by the industry as a possible
method to improve the safety and reliability of structures and
thereby reduce their operational cost. SHM technology is
perceived as a revolutionary method of determining the integrity
of structures involving the use of multidisciplinary fields
including sensors, materials, signal processing, system integration,
and signal interpretation. The core of the technology is the
development of self-sufficient systems for the continuous
monitoring, inspection and damage detection of structures with
minimal labor involvement. The aim of the technology is not
simply to detect structural failure, but also provide an early
indication of physical damage. The early warning provided by an
SHM system can then be used to define remedial strategies
before the structural damage leads to failure. A built-in SHM
system would consist of three major components: 1) Sensors/
sensor networking system, 2) Integrated hardware, and 3)
Software to monitor in-situ condition of on-service structures. In
this study, for the development of this SHM system, PZT-based
active sensing techniques for nondestructive evaluation (NDE)
of steel bridge members are proposed. For steel bridge members
such as steel truss member and bolted joint steel plate, especially,
detection and monitoring of fatigue cracks caused by external
loads is strongly required. Although conventional NDE techniques
such as ultrasonic testing and X-radiography can provide
significant details about the nature of damage, those techniques
usually require direct access to the structure and involve bulky
equipments. Moreover, the techniques usually require disruptions
of the operation of the structures/equipments, which is not
attractive for real-time in-situ SHM. To overcome those
limitations, PZT patches offer special opportunity, because they
are small, light, cheap, and useful as built-in sensor systems. In
this study, two kinds of PZT-based damage detection strategies
have been considered: (a) impedance-based method and (b)
Lamb wave-based method. For the impedance-based method,
successful applications to damage detection for various kinds of
structures have been reported (Ayres et al., 1998; Tseng et al.,
2000; Park et al., 2003; Park et al., 2005). The Lamb wave-based
approach using a pitch-catch method has been utilized by
identifying the changes in the transmission velocity and energy
of the elastic waves associated with damages (Cawley and
Alleyne, 1996; Wait et al., 2004). Firstly, in this paper, PZT
patches were used in conjunction with the impedance and Lamb
waves to detect the presence and growth of artificial cracks on a
1/8 scale model for a vertical truss member of Seongsu Bridge,
Seoul, Korea, which caused the collapse in 1994. Root mean
square (RMS) changes in the impedances and wavelet
coefficients of Lamb waves were found to be good damage
indicators based on the fact they show the rapid and exact
damage estimation results. Secondly, two PZT patches were used
*Ph.D. Candidate, Deprt. of Civil and Environmental Engineering, Korea Adv. Institute of Sci. and Tech., Korea (Corresponding Author, E-mail:
**Member, Prof., Deprt. of Civil and Environmental Engineering, Korea Adv. Institute of Sci. and Tech., Korea (E-mail: [email protected])
***Prof., School of Mechanical Engineering, Kyungpook National University, Korea (E-mail: [email protected])
Seunghee Park, Chung-Bang Yun, and Yongrae Roh
− 34 − KSCE Journal of Civil Engineering
to detect damages on a bolted joint steel plate, which were
simulated by loose bolts. The time of flight (TOF) and wavelet
coefficients (WC) obtained from continuous wavelet transform
of the Lamb wave signals were used. The correlation of the
Lamb wave transmission data with the loose bolts was
investigated. And, the support vector machine was used for
damage classification. A flow chart of these research items are
summarized in Fig. 1.
2. Impedance-based Damage Detection Method
The coupling effect of the electro-mechanical impedance of a
system with PZT and a host structure can be conceptually
investigated as shown in Fig. 2. (Giurgiutiu and Rogers, 1997)
The mechanical aspect of the PZT is described by its short-
circuited mechanical impedance. The host structure is represented
by its driving point mechanical impedance, which includes the
effect of mass stiffness, damping, and boundary conditions. The
PZT is powered by voltage or current. The integrated electro-
mechanical system may be electrically represented by an
electrical impedance which is affected by the dynamics of the
PZT and the host structure. The mechanical impedance, Zs of the
host structure idealized as a SDOF system as in Fig. 2, is defined
as the ratio of a harmonic excitation force F0(ω) at an angular
frequency ω to the velocity response (ω) in frequency domain.
And, similarly, the electrical impedance, ZA of the PZT patch is
defined as the ratio of a harmonic input voltage V(ω) at an
angular frequency ω to the current response I(ω) in frequency
domain. Therefore, the apparent electro-mechanical impedance
of the PZT as coupled to the host structure is given by
(1)
where C is the zero-load capacitance of the PZT and κ31 is the
electromechanical coupling coefficient of the PZT.
The electromechanical impedance technique permits health
monitoring, damage detection, and embedded NDE because it
can measure directly the high frequency local impedance which
is very sensitive to local damage. This method utilizes the
changes that take place in the high-frequency drive-point
structural impedance to identify incipient damage in the
structure. Hence, changes of the mechanical properties of the
host structure may be detected by monitoring the variations of
the electro-mechanical impedance functions shown in Equation
(1). Experimental setup for the impedance-based NDE consists
of an impedance analyzer (HP4294A), a personal computer
which can control matlab programs for data acquisition, signal
processing and damage diagnosis, and PZT patch built-in
structural system as shown in Fig. 3.
3. Lamb Wave-based Damage Detection Method
Lamb waves refer to elastic perturbations propagating in a
solid plate with doubly free boundaries, for which displacements
occur both in parallel and perpendicular to the direction of wave
propagation (Viktorov, 1967). This type of wave phenomenon
was first described in theory by Horace Lamb in 1917. There are
two groups of waves, symmetric and anti-symmetric, that satisfy
the wave equation and boundary conditions and propagate
independently of each other. A graphical representation of those
two groups of waves can be seen in Fig. 4. The waves may
propagate over distances of several meters along a plate-like
structure depending on the material and geometry of the
structure. If a set of transmitting and receiving transducers are
placed on a structure, the received signal contains information
about the integrity along the wave path between two transducers.
Therefore, the present method may be used to monitor a path
rather than a point, and considerable savings in testing time may
be obtained. Since Lamb waves induce stresses throughout the
plate thickness, the entire thickness of the plate can be
interrogated. Unfortunately, however, Lamb wave testing gets
complicated by the dispersive nature of the Lamb waves. Fig. 4
shows the dispersion curves obtained theoretically for the Lamb
waves propagating in a steel plate. The diagram shows that many
wave components with different group velocities exist at the high
frequency range. Therefore, if a structure is excited by a
broadband pulse, many wave components with different
frequencies will travel at different speeds and the pulse shape
x·
Ztotal ω( ) iωC 1 κ312
Zs ω( )ZA ω( ) Zs ω( )+---------------------------------–⎝ ⎠
⎛ ⎞1–
=
Fig. 1. Research Outline of PZT-based Nondestructive Evaluations
Fig. 2. Electro-mechanical System between PZT and Host Struc-
ture (Giurgiutiu and Rogers, 1997)
Fig. 3. Experimental Setup for Impedance-based NDE
Active Sensing-based Real-time Nondestructive Evaluations for Steel Bridge Members
Vol. 10, No. 1 / January 2006 − 35 −
will change as it propagates along the plate. So, attempts have
been made to limit the bandwidth of the excitation to a low
frequency range over which there exist only two fundamental
modes (A0 or S0). An investigation on the dominance of the
fundamental Lamb modes over the proper frequency range for
the steel members was reported (Ghosh et al., 1998). In the
present study, the only A0 mode is intentionally utilized and
investigated. A propagating wave is reflected and/or partially
transmitted, when it encounters a defect or boundary. Then,
damage detection can be carried out based on both the
attenuation and the time delay of the wave component.
Experimental setup for the Lamb wave-based NDE consists of a
Pulser/Reciever (5077PR), a digital oscilloscope (TD2022), a
personal computer which can control matlab programs for data
acquisition, signal processing and damage diagnosis, and PZT
patch built-in structural system as shown in Fig. 5.
3.1 Wavelet Transform for Feature Extraction
The Fourier transform decomposes a signal into its various
frequency components. As it uses the sinusoidal basis functions
that are localized in frequency only, it loses the transient feature
of signals. Therefore, it is necessary to implement the time-
frequency analysis for diagnostics of transient signals induced by
the impulse loading. In time-frequency analysis, the short-time
Fourier transform calculates the local spectral density using
windowing techniques to analyze a small section of the signal at
a time. However, it has a higher resolution in the frequency
domain but a lower resolution in the time domain. Moreover, it is
impossible to simultaneously achieve high resolution in time and
frequency. In order to overcome the limitations of harmonic
analysis, it has been considered to use alternative families of
orthogonal basis functions called wavelets. The continuous
wavelet transform (CWT) decomposes a signal into time and
frequency domain by the dilatation of a wavelet ψ(t) given in the
following equation, where continuous variables a and b are the
scale and translation parameters, respectively (Jeong and Jang,
2000).
(2)
where the asterisk (*) denotes the complex conjugate. In the
present study, a robust wavelet decomposition using “Morlet
wavelet” is employed for the efficient extraction of some
damage sensitive features.
3.2. Support Vector Machines
The Support Vector Machine (SVM) is a mechanical learning
system that uses a hypothesis space of linear functions in a high
dimensional feature space (Vapnik, 1995). The simplest model is
called Linear SVM (LSVM), and it works for data that are
linearly separable in the original feature space only. In the early
1990s, nonlinear classification in the same procedure as LSVM
became possible by introducing nonlinear functions called
Kernel functions without being conscious of actual mapping
space. This extended technique of nonlinear feature spaces is
called Nonlinear SVM (NSVM) shown in Fig. 6. Assume the
training sample S consisting of vectors with i = 1, ..., N,
and each vector xi belongs to either of two classes thus is given a
label . The pair of (w, b) defines a separating hyper-
plane of equation as follows:
(3)
(4)
where w and b are arbitrary constants.
However, Equation (4) can possibly separate any part of the
feature space, therefore one needs to establish an optimal
separating hyper-plane (OSH) that divides S leaving all the
points of the same class on the same side, while maximizing the
margin which is the distance of the closest point of S. The closest
vector xi is called support vector and the OSH (w', b') can be
determined by solving an optimization problem. The resulting
SVM is called Hard Margin SVM. In order to relax the situation,
Hard Margin SVM is generalized by introducing non-negative
slack variables ξ = (ξ1, ξ2, K, ξN) as follows:
Minimize (5)
Subject to
The purpose of the extra term of the CΣξi, where the sum of
i=1, ..., N is to keep under control the number of misclassified
vectors. The parameter C can be regarded as a regularization
parameter. The OSH tends to maximize the minimum distance of
1/w with small C, and minimize the number of misclassified
vectors with large C. To solve the case of nonlinear decision
surfaces, the OSH is carried out by nonlinearly transforming a
set of original feature vectors xi into a high-dimensional feature
space by mapping Φ: xi α zi and then performing the linear
separation. However, it requires an enormous computation of
inner products (Φ(x) · Φ(xi)) in the high-dimensional feature
space. A Kernel function that satisfies the Mercer’s theorem
given in Equation (6) significantly reduces this process. In this
study, a radial basis function machine with convolution function
given in Equation (7) was used as the kernel function (Duda et
al., 2000; Mita and Taniguchi, 2004).
(6)
Wf b a,( ) x∞–
∞
∫ t( )1
a------ψ*
t b–
a---------⎝ ⎠⎛ ⎞dt=
xi Rn∈
yi 1– 1,{ }∈
S x1 y1,( ) … xN yN,( ), ,( )=
w x⋅( ) b+ 0=
d w'( )1
2--- w' w'⋅( ) C ξi∑+=
yi w' xi⋅( ) b'+( ) 1 ξi–≥
Φ x( ) Φ xi( )⋅( ) K x xi,( )=
Fig. 4. Lamb modes and Dispersion Curves
Fig. 5. Experimental Setup for Lamb Wave-based NDE
Seunghee Park, Chung-Bang Yun, and Yongrae Roh
− 36 − KSCE Journal of Civil Engineering
(7)
4. Verification of the Proposed Methods
4.1 Experimental Study I: Crack Monitoring on Steel
Truss Member
First experimental study was carried out to check the
feasibility of crack detection using PZT patches built in on the
structural member. PZT patches were used in conjunction with
the impedance and Lamb waves to detect the presence and
growth of artificial cracks on a 1/8 scale model for a vertical truss
member of Seongsu Bridge, Seoul, Korea, which caused the
collapse in 1994 (Fig. 7(a)). The original member consists of two
segments with wide flange sections of different flange thickness
welded together. Fatigue cracks developed at the welded zone of
two flanges, and caused eventual sever of the member.
In the impedance approach, five PZT patches of 25×15×0.5
mm were attached to the outside surface of a flange as shown in
Fig. 7(b). Damages were inflicted by cutting the flange at 2
locations sequentially, and 3 damage cases were constructed.
The impedance signatures obtained at the PZT patch #2 in each
damage case are shown in Fig. 7(b). The RMS (root mean
square) changes of the impedance signatures were considered as
the damage indicators (Equation (8)).
(8)
where Z(ωi) is the post-damage impedance signature at the i-th
measurement point and Z0(ωi) is the corresponding pre-damage
value. The results indicate that the RMS changes of the impedance
signatures according to the cracks give good information for
damage localization and severity.
In the Lamb wave propagation approach, a sensor networking
system composed of four PZT patches was embedded to the
surface as shown in Fig. 7(c). In order to detect and monitor two
cracks with different lengths inflicted artificially, four pair pitch-
catch signals of the Lamb wave propagations (#1 to #2, #1 to #4,
#3 to #2, and #3 to #4) were analyzed according to the initiation
and growth of crack. For damage scenario, two cracks were
artificially inflicted up to 4cm from both sides by the increment
of 1 cm. So, totally, 8cm crack was cut. In order to extract
damage sensitive features, the continuous wavelet transform was
applied as a robust signal processing technique, and wavelet
coefficients based on peak values were selected. Statistically,
RMS changes in the wavelet coefficients of four pair pitch-catch
signals were considered according to the damage states. Damage
indicator obtained by a least square curve fitting algorithm was
found to increase proportionally to the crack length as shown in
Fig. 7(c).
The above experimental results verified the efficacy and the
robustness of the proposed approaches, emphasizing the great
potential for developing an automated, real-time and in-situ health
monitoring system for application to large civil infrastructures.
Both approaches would be enhanced by the use of pattern
recognition algorithm that can estimate and classify damages
based on a learning process.
4.2 Experimental Study II: Loose Bolt Monitoring on
Jointed Steel Plates
Second experimental study has 2 objectives: (1) to extract the
efficient feature vectors from wavelet transform of Lamb wave
signals, and (2) to improve the damage detection performance by
using the SVMs trained by a set of the feature vectors. An
experimental setup and its overall configuration are shown in
Fig. 8. The specimen (700×100×2mm) was made of 2 steel
plates (400×100×2mm) jointed. Eight steel bolts with 10mm in
diameter with washers and nuts were used. Two PZTs were
placed at locations 100mm apart from the ends. The distance
between two PZTs is 475mm. The dimension of each PZT patch
is 35×25×0.2mm. An impulse waveform was applied to PZT 1
serving as a transmitter, and the propagating wave signal was
measured at PZT 2 serving as a sensor. The exciting frequency
by the PZT patch was found as 23.4 kHz. It is noted that the most
Lamb waves tend to propagate along with the path (area between
two red dotted lines) which depends on the width of the PZT
patch as in Fig. 6. Therefore, it can be expected that damages out
of the Lamb wave path (damages out of path, DOP) do not cause
significant changes in the Lamb wave signal compared with the
case of damages in the Lamb wave path (damages in path, DIP).
Damages were introduced by removing several bolts from the
joints. At first, the test was carried out on the intact state of the
bolted joints, and then experiments were performed on 8
different damage cases. The continuous wavelet transform
technique also was explored for detecting the changes in the
dispersive Lamb waves due to damages as in example study I.
The TOF (time of flight) and WC (wavelet coefficient) were
obtained based on peak values. The results were obvious that
damages in the Lamb wave path (as in Bolts 2, 3, 6 and 7) caused
significant changes in TOF and WC, while damages out of the
Lamb wave path (as in Bolts 1, 4, 5 and 8) did not. That is, for
K x xi,( ) expx xi– 2
σ2
-----------------–⎝ ⎠⎛ ⎞=
RMSD %( )Σi 1=
i N= Z ωi( ) Z0 ωi( )–( )2
Σi 1=i N= Z0 ω i( )( )2
-------------------------------------------------- 100×=
Fig. 6. Non-linear Support Vector Machine
Active Sensing-based Real-time Nondestructive Evaluations for Steel Bridge Members
Vol. 10, No. 1 / January 2006 − 37 −
the former cases, TOF and WC gave good representation for
identifying of localized damages. For the latter cases, however,
their variations did not give consistent trend correlating with
damages.
To improve the damage detection performance for the latter
cases, the proposed pattern recognition technique, SVM was
investigated. Firstly, three damage classes were introduced
considering damage locations, as described in Table 2. Totally,
Fig. 7. Experimental Study I: Crack Detection on Welded Zone
Fig. 8. Experimental Configuration
Table 1. Damage Scenario
Damage Cases Locations of Loosened Bolts
Case 1 #1
Case 2 #2
Case 3 #1 & 4
Case 4 #2 & 3
Case 5 #1,2,3 & 4
Case 6 #1,2,3,4,5 & 8
Case 7 #1,2,3,4,5 & 6
Case 8 #1,2,3,4,5,6 & 7
Seunghee Park, Chung-Bang Yun, and Yongrae Roh
− 38 − KSCE Journal of Civil Engineering
120 patterns to train SVMs were prepared by forty samples with
1 bolt removed from each class. They composed a 2D feature
space as shown in Fig. 9. From Fig. 9, it can be noted that the
distinctions of each class’s regions are very ambiguous.
Therefore, probabilistic decision-making (the establishment of
optimal decision boundaries) between three classes were
strongly required. Fig. 10 shows three kinds of classifying cases
with different combinations of classes, and the optimal decision
boundaries for each case were constructed on high dimensional
feature space. To verify the effectiveness of the SVM-based
classifier, 20 test patterns prepared by ten arbitrary samples with
1 loose (not removed) bolt from Classes 2 (DOP) and 3 (DIP)
were also used, and the results are showed in Fig. 11. It can be
founded that the SVM gave very good detection performance for
not only DIP (detection rate: 100%) but also DOP (detection
rate: 90%).
5. Conclusions
The applicability of piezoelectric lead-zirconate-titanate(PZT)-
based nondestructive evaluation (NDE) techniques for steel
bridge members has been presented from experimental studies.
The work presented in this paper demonstrates that the
impedance-based damage detection method and Lamb wave-
based damage detection method applied with PZT patches are
both able to detect the damages such as cracks and loose bolts on
steel members. The sensor networking system composed of PZT
patches was built in to the host structure and used to record the
electromechanical impedance and the Lamb wave propagation
data. Hence, if there was any defect in the host structures, the
data obtained from the PZT patches would be modified by the
presence of the defects. The present approaches yield an
improved methodology for real-time damage detection and
monitoring in critical members of steel bridges. The real-time
smart NDE methods presented here can be applied to real steel
structures. One can envision in-situ networking systems
composed of PZT patches being placed on critical members to
detect the presence and growth of real damages. Through local-
area data collection, interpretation, and automatic system for
health monitoring and damage estimation can be devised and
installed. The use of the proposed damage index will allow rapid
estimation and automatic assessment of the structural health
condition in terms of a single scalar value. Important safety
enhancement and significant cost savings are predicted through
Table 2. Three Classes Considering Damage State
Classes Descriptions
1 Intact Case
2 Damages out of Lamb wave path (DOP)
3 Damages in Lamb wave path (DIP)
Fig. 9. Preliminary Test Results for Training Patterns
Fig. 10. Feature Space Divided by SVMs
Fig. 11. SVM-based Damage Estimation Results
Active Sensing-based Real-time Nondestructive Evaluations for Steel Bridge Members
Vol. 10, No. 1 / January 2006 − 39 −
the wide area implementation of this novel method for structural
health monitoring, damage detection, and failure prevention.
Acknowledgements
The study was jointly supported by the Smart Infra-Structure
Technology Center (SISTeC) at KAIST sponsored by the Korea
Science and Engineering Foundation (KOSEF), and the Infra-
Structure Assessment Research Center (ISARC) sponsored by
Ministry of Construction and Transportation (MOCT), Korea.
Their financial supports are greatly acknowledged.
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