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