A wavelet-based Bayesian damage identification technique using an evolutionary algorithm. Issue 4 (1st December 2016) - Varmazyar, Maryam; Haritos, Nicholas; Kirley, Michael
Abstract: 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.