A Bayesian approach for site-specific extreme load prediction of large scale bridges

To measure uncertainties of site-specific extreme loads for a signature bridge, a probabilistic method is developed based on structural health monitoring data in the Bayesian context. Compared with respective design loads, the probability of the predicted extreme load exceeding the design one is used to determine whether the design load is sufficient. The generalized Pareto distribution was first employed to model extreme value information. Then, Bayesian estimation was applied to predict probabilistic distributions of extreme loads, accounting for uncertainties within the prediction process. Finally, the effectiveness of the proposed method was validated through wind speed and temperature measurements from the Nanjing Dashengguan Yangtze River Bridge. The extreme wind speed and four types of thermal loads were predicted based on site-specific monitored loading data by using Bayesian estimation of the generalized Pareto distribution. As a result, the design wind speed and design change in uniform temperature are sufficient for the signature bridge. However, the probability that the predicted extreme vertical girder temperature difference exceeds the design one is 99.96%, and the probabilities subject to the transverse girder temperature difference and tower temperature difference are almost 100%.