A wavelet-based Bayesian damage identification technique using an evolutionary algorithm
| Autor: | Nicholas Haritos, Michael Kirley, M. Varmazyar |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
business.industry Mechanical Engineering Bayesian probability Probabilistic logic Evolutionary algorithm Wavelet transform 02 engineering and technology 01 natural sciences Noise Wavelet Mechanics of Materials 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing CMA-ES business Evolution strategy 010301 acoustics Algorithm Civil and Structural Engineering |
| Zdroj: | Australian Journal of Structural Engineering. 17:225-241 |
| ISSN: | 2204-2261 1328-7982 |
| DOI: | 10.1080/13287982.2016.1259712 |
| Popis: | Structural damage identification is a challenging task, especially when response measurements have local discontinuities and display non-stationarity. This paper presents a one-stage model-based damage identification technique using wavelet power spectra to address this problem. To detect, locate and estimate the severity of damage, the finite element model is updated using a Bayesian probabilistic approach and a covariance matrix adaptation evolution strategy, taking into account the uncertainty caused by measurement noise and modelling error. A range of numerical simulations are used to evaluate the efficacy of the model under different damage scenarios, including: both single and multiple damage locations; varying damage severity; the introduction of noise and modelling errors and incompleteness in the number of captured modes and measurement response data applied to a beam structure. The results obtained across the damage scenarios are observed to be robust. A comparison against existing power... |
| Databáze: | OpenAIRE |
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