Bayesian wavelet methodology for structural damage detection

Accurate structural damage detection is still a challenging problem due to the complicated nonlinear behavior of structural system, incomplete sensed data, presence of noise in the data, and uncertainties in both experimental measurement and analytic model. This paper presents a Bayesian wavelet probabilistic methodology for nonparametric damage detection to address the above-mentioned issues. A Bayesian discrete wavelet packet transform-based denoising approach is employed to perform data cleansing prior to damage detection. A nonparametric system identification method, based on fuzzy wavelet neural networks, is applied to predict dynamic responses of the structure subjected to external excitation. Bayesian hypothesis testing is developed to assess the difference between the sensed data and model prediction. The Bayesian assessment metric is treated as a random variable and its probability density function is constructed using Monte Carlo simulation technique to incorporate possible uncertainties. The evaluation method provides quantitative information about the condition of a structural system. The methodology is validated using the sensed data collected from a 5-story test steel frame and a 38-story concrete building model. Both the original and denoised data are used in the damage detection to investigate the effects of noise on detection accuracy. Numerical results demonstrate that the proposed methodology provides an effective metric to quantify the confidence in the damage detection. Copyright © 2007 John Wiley & Sons, Ltd.

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