Prediction of Extreme Traffic Load Effects of Bridges Using Bayesian Method and Application to Bridge Condition Assessment

Abstract

Due to the aging of transportation infrastructures and the ever-increasing traffic, the condition assessment of bridges has become increasingly important because it provides useful information for bridge management. A reliable condition assessment depends on the accurate prediction of extreme traffic load effects (LEs) in the remaining life of bridges. In this study, the Bayesian method is introduced for the prediction of extreme traffic LEs to improve the reliability of the prediction, and a framework for bridge condition assessment making use of the predicted LEs is proposed. To demonstrate the proposed methodology, a case study on the condition assessment of the new I-10 Twin Span Bridge (TSB) using structural health monitoring data is presented. The results show that the Bayesian method can provide more reliable predictions compared with the conventional method, because it quantifies the uncertainties inherent in the parameters and incorporates these uncertainties into the prediction. Based on the predicted traffic LEs, the condition of the bridge is assessed using the proposed framework.