A Bayesian methodology for crack identification in structures using strain measurements

Abstract

A Bayesian system identification methodology is presented for estimating the crack location, size and orientation in a structure using strain measurements. The Bayesian statistical approach combines information from measured data and analytical or computational models of structural behaviour to predict estimates of the crack characteristics along with the associated uncertainties, taking into account modelling and measurement errors. An optimal sensor location methodology is also proposed to maximise the information that is contained in the measured data for crack identification problems. For this, the most informative, about the condition of the structure, data are obtained by minimising the information entropy measure of the uncertainty in the crack parameter estimates. Both crack identification and optimal sensor location formulations lead to highly non-convex optimisation problems in which multiple local and global optima may exist. A hybrid optimisation method, based on evolutionary strategies and gradient-based techniques, is used to determine the global minima. The effectiveness of the proposed methodologies is illustrated using simulated data from a single crack in a thin plate subjected to static loading.