Vehicle traffic plays an important role in fatigue deterioration and overloadleading to the collapse of bridges. The monitored data show that occurrences ofvehicle loads are correlated. Additionally, it is more reasonable to employ the tailregion of a distribution when estimating extreme loads. A novel de-correlated tailbasedextreme value (EV) distribution model is proposed in this paper. Moreover, aBayesian form of this new model is constructed, and an extension of this model, theConfidence Index (CI), is defined and may be promising for applications. Themonitored vehicle weight on the Nanjing 3rd Yangtze River Bridge is used todemonstrate that the proposed tail-based de-correlated EV model predicts the extremeload more accurately than traditional methods and that the Bayesian approach canfurther increase the precision of this estimate. Finally, the calculated CI of thecomplete prediction process offers a comprehensive guideline for the estimateprecision.
展开▼
