and Applied Mechanics
0, 0, pp. , Warsaw 0
Experimental damage assessment of support condition for plate structures using wavelet transform
identifying damage in structures using vibration-based damage detection (VBDD) techniques, owing
to their ability to identify singularities by decomposing mode shapes structural responses of the
structure. In VBDD, the support condition of a structure influences the structural responses and modal
properties. In fact, structural responses and modal properties are a lot more sensitive to changing
boundary conditions than to crack and fatigue damage, resulting in inaccurate damage detection
results. Therefore, in this study, sensitivity tests to estimate a suitable distance range which allows
damage detection by imposing single support damage were carried out. The estimated appropriate
distance was then applied to detect damage at multiple supports. This involved the applicability of
response acceleration of plate structures to support assessment by applying continuous wavelet
transform (CWT) and discrete wavelet transform (DWT). The damage cases were introduced by
releasing bolts at the specified fixed supports of the plate to simulate the damage. The response
accelerations of the rectangular plate at points close to the supports were measured and decomposed
using CWT and DWT to assess the structural integrity of each support. The results showed that an
appropriate distance range is necessary for accurate damage detection, and both, CWT and DWT can
provide reliable outputs. However, the first- and fourth-level detail coefficients of DWT fail to
indicate damage in some cases. A more detailed investigation of the effect of different wavelet scale
ranges on damage detection using CWT demonstrated that the accuracy of damage detection increases
as the scale decreases.
Asgarian B., Aghaeidoost V., Shokrgozar H.R., 2016, Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform, Marine Structures, 45, 1-21
Bagheri A., Amiri G.G., Khorasani M., Bakhshi H., 2011, Structural damage identification of plates based on modal data using 2D discrete wavelet transform, Struct. Eng & Mech., 40, 13-28.
Bakhary N., Hao H., Deeks A.J., 2010, Substructuring technique for damage detection using statistical multi-stage artificial neural network, Advances in Structural Engineering, 13, 619-39.
Bakir P.G., Eksioglu E.M., Alkan S., 2012, System identification of a reinforced concrete building using the complex mode indicator function & the hilbert transform techniques, Testing & Eva., 40, 427-34
Brunesi E., Nascimbene R., Rassati G.A., 2013, Evaluation of the response of partially restrained bolted beam-to-column connection subjected to cyclic pseudo-static loads, Structures Congress,
Cantero D., Basu B., 2015, Railway infrastructure damage detection using wavelet transformed acceleration response of traversing vehicle, Struct. Cont. & Health Mon., 22, 62−70.
Cao M., Xu W., Ostachowicz W., Su Z., 2014, Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature, J of Sound & Vib., 333, 1543–53.
Douka E., Loutridis S., Trochidis A., 2003, Crack identification in beams using wavelet analysis, International Journal of Solids and Structures, 40, 3557-69.
Gałka J., Ziółko M., 2009, Wavelet parametrization for speech recognition, InProceedings of an ISCA tutorial and research workshop on non-linear speech processing NOLISP.
Gentile A., Messina A., 2003, On the continuous wavelet transforms applied to discrete vibrational data for detecting cracks in damaged beams, Int’l J. of Solids & Struct., 40, 295–315.
Hester D., González A., 2012, A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle, Mechanical Systems and Signal Processing, 28, 145-166.
Hong J.C., Kim Y.Y., Lee H.C., Lee Y.W., 2002, Damage detection using the Lipschitz exponent estimated by the wavelet transform: applications to vibration modes of a beam, Int’l J. of solids and structures. 39, 1803-1816.
Lee Y.U., Kim Y.Y., Lee H.C., 2000, Damage Detection in a Bean Via the Wavelet Transform of Mode Shapes, Transactions of the Korean Society of Mechanical Engineers A, 24, 916-25.
Li J., Hao H., 2016, Health monitoring of joint conditions in steel truss bridges with relative displacement sensors, Measurement, 88, 360-371.
Liu S., Du C., Mou J., Martua L., Zhang J., Lewis F.L., 2014, Diagnosis of structural cracks using wavelet transform and neural networks, NDT & E International, 54, 9–18.
Mallat S., 1998, A Wavelet Tour of Signal Processing. Academic Press, New York.
Ni Q.Q., Iwamoto M., 2002, Wavelet transform of acoustic emission signals in failure of model composites, Engineering Fracture Mechanics, 69, 717-28.
Ovanesova A.V., Suárez L.E., 2004, Applications of wavelet transforms to damage detection in frame structures, Engineering Structures, 26, 39-49.
Peng X.L., Hao H., Li Z.X., Fan K.Q., 2013, Experimental study on subsea pipeline bedding condition assessment using wavelet packet transform, Engineering Structures, 31, 81-97.
Rainieri C., Fabbrocino G., Manfredi G., Dolce M., 2012, Robust output-only modal identification and monitoring of buildings in the presence of dynamic interactions for rapid post -earthquake emergency management, Engineering Structures, 34, 436–446.
Reda Taha M.M., Noureldin A., Lucero J.L., Baca T.J., 2006, Wavelet transform for structural health monitoring: a compendium of uses and features, Structural Health Monitoring, 5, 267-295.
Rucka M., Wilde K., Application of continuous wavelet transform in vibration based damage detection method for beams and plates, Journal of Sound and Vibration, 297, 536-550.
Vafaei M., Alih S.C., Rahman A.B.A., Adnan B.A., 2015, A wavelet-based technique for damage quantification via mode shape decomposition, Structure & Infrastructure Engr’g, 11, 869-883.
Siringoringo D.M., Fujino Y., 2008, System identification of suspension bridge from ambient vibration response, Engineering Structures, 30, 462–77.
Staszewski W.J., 1998, Structural and mechanical damage detection using wavelets, The Shock and Vibration Digest, 30, 457–472.
Staszewski W.J., Pierce S.G., Worden K., Philp W.R., Culshaw B., 1997, Wavelet signal processing for enhanced Lamb-wave defect detection in composite plates using optical fiber detection, Opt. Eng. 36, 1877-1888.
Vafaei M, Adnan A., 2014, Seismic damage detection of tall airport traffic control towers using wavelet analysis, Structure and Infrastructure Engineering, 10, 106-127.
Vafaei M., Adnan A., Rahman A.B.A., 2014, A neuro-wavelet technique for seismic damage identification of cantilever structures, Structure and Infrastructure Engineering, 10, 1666-1684.
Wu J.D., Chen J.C., 2006, Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines, NDT & E International, 39, 304–11
Yang J.N., Xia Y., Loh C.H., 2014, Damage identification of bolt connections in a steel frame, J. Structural Engineering, 140, 04013064.
Yang Y., Nagarajaiah S., 2014, Blind identification of damage in time-varying systems using independent component analysis with wavelet transform, Mech. Sys & Sig. Processing, 47, 3–20.
Yu L., Zhu J., 2015, Structural damage detection of truss bridge under environmental variability, Appl. Math. Inf. Sci., 9, 259-265.
Yu Z., Xia H., Goicolea J.M., Xia C., 2016, Bridge damage identification from moving load induced detection based on wavelet transform & lipschitz exponent, Int’l. J. of Stru. Sta. & Dyn., 16, 1550003.
Zhong S., Oyadiji O., 2011, Crack detection in simply supported beams using stationary wavelet transform of modal data, Structural Control and Health Monitoring, 18, 169-190.