Application of Adaptive Wavelet Transform based Multiple Analytical Mode Decomposition for Damage Progression Identification of Cable-Stayed Bridge via Shake Table Test
Abstract
Extracting useful information (damage existence, location, identification, and quantification) from measured signals for damage identification is critical in structural health monitoring, while time-varying nature of most signals often require huge efforts. In this paper, adaptive wavelet analysis AWT is first introduced as a preprocessing approach of clearer, smoother and more accurate time–frequency representation. Optimized analytical mode decomposition (AMD) is then utilized for signal component extraction, with the help of AWT for bisecting frequency determination. Examples of time-varying signals of sinusoidal function and Duffing systems are used to illustrate the advantages of the algorithm, which proves to be successful in signal decomposition. Multiple AMD (MAMD) with the optimized algorithm is then utilized together with AWT for signal decomposition and system identification of the shake table test of a 1/20-scale cable-stayed bridge model. The extracted stiffness and damping coefficients retain a preliminary indication of the damage progression during the earthquake input.
Recommended Citation
H. Qu et al., "Application of Adaptive Wavelet Transform based Multiple Analytical Mode Decomposition for Damage Progression Identification of Cable-Stayed Bridge via Shake Table Test," Mechanical Systems and Signal Processing, vol. 149, Elsevier Ltd, Feb 2021.
The definitive version is available at https://doi.org/10.1016/j.ymssp.2020.107055
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Keywords and Phrases
Adaptive algorithm; Cable-stayed bridge; Hilbert transform; Shake table test; Signal processing; System identification; Wavelet transform
International Standard Serial Number (ISSN)
0888-3270; 10961-216
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier Ltd, All rights reserved.
Publication Date
15 Feb 2021
Comments
Financial support was provided by the U.S. National Science Foundation (Award No. CMMI1538416), National Natural Science Foundation of China (Award No. 51478338), and National Basic Research Program of China “973 Program” (Award No. 2013CB036302).