| Autor: |
Varmazyar, Maryam, Haritos, Nicholas, Kirley, Michael |
| Předmět: |
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| Zdroj: |
Australian Journal of Structural Engineering; 2016, Vol. 17 Issue 4, p225-241, 17p, 1 Diagram, 5 Charts, 11 Graphs |
| Abstrakt: |
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 spectral density-based methods, which are only applicable in the case of stationary data, indicates that the proposed approach outperforms in almost all damage conditions. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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