Threshold selection for extreme strain extrapolation due to vehicles on bridges

Abstract

In the design and condition assessment of bridges, the extreme vehicle load effects are necessary to be taken into consideration which may occur during the service period of bridges. In order to obtain an accurate extrapolation of the extreme value based on limited duration, threshold selection is a critical step in the peak-over-threshold method. Overly high threshold results in little information to be used and excessively low threshold leads to large bias in parameters estimation of generalized Pareto distribution. To investigate this issue, 417 days of strain data acquired from the long-term structural health monitoring system of Taiping Lake Bridge in China are employed in this paper. According to the tail distribution of the strain data induced by vehicle loads, four homothetic distributions are chosen as the parent distributions, from which lots of random samples are generated by the Monte Carlo method. For each parent distribution, the 100-yearly extreme values at different thresholds are estimated and compared with the theoretical value based on those samples. Then a simple and empirical threshold selection method is proposed, which can be an effective threshold selection tool for the extreme value estimation of vehicle load effect in future engineering practice.