Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing

This paper proposes a new framework for predicting remaining bridge strength that integrates a Bayesian network and in situ load testing. It discusses the uncertainty of important factors on corrosion damage and develops a stiffness degradation model for corroded beams based on experimental investigations. Following this, the authors develop a Bayesian network that includes corrosion damage, stiffness degradation, load-deflection response, and other factors to predict structural strength degradation. A numerical example using an existing RC bridge demonstrates the general procedures. The comparison between the theoretical and the experimental deflections from load testing shows that the proposed methodology can efficiently improve prediction accuracy and reduce prediction uncertainty.