Experimental damage assessment of support condition for plate structures using wavelet transform
More details
Hide details
Construction Research Centre, Universiti Teknologi Malaysia, Johor, Malaysia
School of Civil Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
Forensic Engineering Center, Universiti Teknologi Malaysia, Johor, Malaysia
Publish date: 2019-04-15
Submission date: 2017-07-05
Acceptance date: 2019-02-03
Journal of Theoretical and Applied Mechanics 2019;57(2):501–518
Wavelet transforms (WTs) have gained popularity due to their ability to identify singu- larities by decomposing mode shapes of structures. In VBDD, the support condition of a structure influences structural responses and modal properties. In fact, the structural re- sponses 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 are carried out. The estimated appropriate di- stance is 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 have been introduced by releasing bolts at 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 was necessary for accu- rate damage detection, and both, CWT and DWT could provide reliable outputs. However, the first- and fourth-level detail coefficients of DWT failed to indicate damage in some ca- ses. A more detailed investigation of the effect of different wavelet scale ranges on damage detection using CWT demonstrated that the accuracy of damage detection increased as the scale decreased.
Abdulkareem M., Bakhary N., Vafaei M., Noor N.M., Padil K.H., 2018, Non-probabilistic wavelet method to consider uncertainties in structural damage detection, Journal of Sound and Vibration, 433, 77-98.
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, 619-639.
Bakir P., Eksioglu M., Alkan S., 2012, System identification of reinforced concrete building using the complex mode indicator function and the Hilbert transform technique, Testing And Evaluation, 40, 427-434.
Cantero D., Basu B., 2015, Railway infrastructure damage detection using wavelet transformed acceleration response of traversing vehicle, Structural Control and Health Monitoring, 22, 62-70.
Cao M., Xu W., Ostachowicz W., 2014, Damage identification for beams in noisy condition based on Teager energy operator-wavelet transform modal curvature, Journal of Sound and Vibration, 333, 1543-1553.
Chen J., Rostami J., Peter T., Wan X., 2017, The design of a novel mother wavelet that is tailor-made for continuous wavelet transform in extracting defect-related features from reflected guided wave signals, Measurement, 110, 176-191.
Douka E., Loutridis S., Trochidis A., 2003, Crack identification in beams using wavelet analysis, International Journal of Solids and Structures, 40, 3557-3569.
Gentile A., Messina A., 2003, On the continuous wavelet transforms applied to discrete vibrational data for detecting cracks in damaged beams, International Journal of Solids and Structures, 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, Solids and Structures, 39, 1803-1816.
Lee Y.U., Kim Y.Y., Lee H.C., 2000, Damage detection in a beam via the wavelet transform of mode shapes, Transactions of the Korean Society of Mechanical Engineers A, 24, 916-925.
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.
Ovanesova A.V., Su´arez L.E., 2004, Applications of wavelet transforms to damage detection in frame structures, Engineering Structures, 26, 39-49.
Padil K.H., Bakhary N., Hao H., 2017, The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection,Mechanical Systems and Signal Processing , 83, 194-209.
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., 2006, Application of continuous wavelet transform in vibration based damage detection method for beams and plates, Journal of Sound and Vibration, 297, 536-550.
Siringoringo D.M., Fujino Y., 2008, System identification of suspension bridge from ambient vibration response, Engineering Structures, 30, 462-477.
Staszewski W.J., 1998, Structural and mechanical damage detection using wavelets, The Shock and Vibration Digest, 30, 457-472.
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 and Infrastructure Engineering, 11, 869-883.
Yang J.N., Xia Y., Loh C.H., 2014, Damage identification of bolt connections in a steel frame, Journal of Structural Engineering, 140, 04013064.
Yang Y., Nagarajaiah S., 2014, Blind identification of damage in time-varying systems using independent component analysis with wavelet transform, Mechanical Systems and Signal Processing, 47, 3-20.
Yu L., Zhu J., 2015, Structural damage detection of truss bridge under environmental variability, Applied Mathematics and Information Sciences, 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 and Lipschitz exponent, International Journal of Structural Stability and Dynamics, 16, 1550003.
Zhang X., Feng N., Wang Y., Shen Y., 2015, Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy, Journal of Sound and Vibration, 339, 419-432.
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.
Ziopaja K., Pozorski Z., Garstecki A., 2011, Damage detection using thermal experiments and wavelet transformation, Inverse Problems in Science and Engineering, 19, 127-153.