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• W3049063750 endingPage "107055" @default.
• W3049063750 startingPage "107055" @default.
• W3049063750 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." @default.
• W3049063750 created "2020-08-21" @default.
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• W3049063750 date "2021-02-01" @default.
• W3049063750 modified "2023-10-12" @default.
• W3049063750 title "Application of adaptive wavelet transform based multiple analytical mode decomposition for damage progression identification of Cable-Stayed bridge via shake table test" @default.
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• W3049063750 doi "https://doi.org/10.1016/j.ymssp.2020.107055" @default.
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