A multi-scale framework for probabilistic structural analysis of PSC girders considering pit corrosion of prestressing wires

Abstract

Many standards and studies have emphasized the importance of considering various uncertainty factors of material properties and corrosion in assessing the structural performance of bridges. In such an analysis, however, high computational cost is a major issue because a probabilistic structural analysis requires multiple structural analyses. In this study, an efficient framework for constructing a probabilistic structural analysis model is proposed. First, a surrogate model is constructed using Gaussian process regression for the given structural analysis model. Next, random samples of model inputs and structural responses, which are assumed to be correlated non-Gaussian random variables, are generated based on the Cholesky decomposition and Morgenstern model. Finally, the statistical properties of structural responses are derived by a goodness-of-fit test; hence, they can be considered as probabilistic inputs for an upper-scale model in the form of random variables. This three-step process can be introduced accumulatively from the lower-scale to upper-scale models about a structure; accordingly, it is termed as a multi-scale framework for probabilistic structural analysis. The proposed framework was applied to construct probabilistic models for prestressed concrete (PSC) girders with corroded prestressing wires, and the probabilistic prediction results for the ultimate load and displacement showed a good agreement with the experimental data.