Hierarchical bayesian model updating for probabilistic damage identification

Abstract

This paper presents the newly developed Hierarchical Bayesian model updating method for identification of civil structures. The proposed updating method is suitable for uncertainty quantification of model updating parameters, and probabilistic damage identification of the structural systems under changing environmental conditions. The Bayesian model updating frameworks in the literature have been successfully used for predicting the “parameter estimation uncertainty” of model parameters with the assumption that there is no underlying inherent variability in the updating parameters. However, different sources of uncertainty such as changing ambient temperature or wind speed, and loading conditions will introduce variability in structural mass and stiffness of civil structures. The Hierarchical Bayesian model updating is capable of predicting the underlying variability of updating parameters in addition to their estimation uncertainty. This approach is applied for uncertainty quantification and damage identification of a three-story shear building model. The proposed updating framework is finally implemented for uncertainty quantification of model updating results based on experimentally measured data of a footbridge which is exposed to severe environmental conditions. In this application, the stiffness parameter of the model is estimated as a function of measured temperature through the Hierarchical framework. © The Society for Experimental Mechanics, Inc. 2015.