Αναγνώριση ρωγμών σε κατασκευές με βέλτιστη τοποθέτηση αισθητήρων

Abstract

A Bayesian system identification methodology is presented for estimating the crack location, size and orientation using strain measurements. The Bayesian statistical approach combines information from measured data and analytical or computational models of structural behavior to predict estimates of the crack characteristics along with the associated uncertainties, taking into account modeling and measurement errors. An optimal sensor location methodology is proposed to maximize 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 minimizing the information entropy measure of the uncertainty in the model parameter estimates provided by the above statistical system identification method. Both crack identification and optimal sensor location formulations lead to highly non-convex optimization problems in which multiple local and global optima may exist. A hybrid optimization method based on evolutionary strategies and gradient based techniques is used to determine the global minimum. The effectiveness of the proposed methodologies is illustrated using simulated data from a single crack in a thin plate subjected to known and unknown static loading. The effects of modeling and measurement error on the effectiveness of the crack detection method, as well as the methodology’s limitations are investigated.