Bayesian finite element model updating for crack growth

Abstract

This paper describes a probabilistic structural health monitoring framework to determine crack growth on structural members using model updating. The framework uses Bayesian inference to estimate crack lengths. On the proposed framework data from embedded piezoelectric wafer sensors (PWAS) and acoustic emission sensors is used for model updating. This paper presents preliminary results obtained using simulated data of a steel specimen. As a first step, the crack length is estimated using calculated displacements at the tip of the specimen. Results show that Bayesian inference can be used to estimate crack lengths on structural members. ©2010 Society for Experimental Mechanics Inc.