Quantifying Uncertainties in Model Updating Following Bayesian Approach Using a Parameter Space-Search Algorithm

Abstract

AbstractModel updating aims to provide accurate models to reveal possible structural damage information for structural health monitoring (SHM). Uncertainties always exist in model updating due to incomplete information in measurement and modeling. These uncertainties usually cause the problem of non-uniqueness, i.e., multiple equivalent models can fit the experimental data the same well. Locating all these equivalent models and including them for representing structural dynamics is a challenging task. This work employs a Bayesian probabilistic framework for model updating, so that the uncertainties can be quantified, and thus all of the multiple equivalent models can be considered naturally. A parameter space-search algorithm is proposed to systematically locate all the equivalent models. A transmission tower under laboratory conditions with limited modal parameters was used to valid the proposed method.