Long-term bridge health monitoring and performance assessment based on a Bayesian approach

Abstract

This study presents a damage detection approach for the long-term health monitoring of bridge structures. The Bayesian approach comprising both Bayesian regression and Bayesian hypothesis testing is proposed to detect the structural changes in an in-service seven-span steel plate girder bridge with Gerber system. Both temperature and vehicle weight effects are accounted in the analysis. The acceleration responses at four points of the bridge span are utilised in this investigation. The data covering three different time periods are used in the bridge health monitoring (BHM). Regression analyses showed that the autoregressive exogenous model considering both temperature and vehicle weight effects has the best performance. The Bayesian factor is found to be a sensitive damage indicator in the BHM. The Bayesian approach can provide updated information in the real-time monitoring of bridge structures. The information provided from the Bayesian approach is convenient and easy to handle compared to the traditional approaches. The applicability of this approach is also validated in a case study where artificially generated damage data is added to the observation data.