Structural damage identification using exponential function models, integral transform and Bayesian inference

Abstract

The present work addresses the inverse problem of structural damage identification in an Euler-Bernoulli beam. The structural damage field in the direct model is continually described by exponential basis functions and the model’s dynamic response is obtained through a hybrid solution (analytical-numerical) provided by the Generalized Integral Transform. The inverse damage identification problem is formulated according to Bayesian inference and the Transitional Markov Chain Monte Carlo method is used to sample the posterior probability density function of the uncertain parameters that describe the damage field. The verification of the proposed methodology is based on numerical simulations considering a simply supported Euler-Bernoulli beam and three different damage scenarios.