Investigation of multiple-presence factor for traffic loads on road-rail bridges based on a novel extreme value analysis approach

Abstract

Multiple-presence factor (MPF) is an efficient measure for calculating traffic load effects on bridges with multiple lanes. It takes into account the probability of multiple heavy vehicles that simultaneously present in adjacent lanes. Though road-rail bridges carrying both road and rail traffic are common in modern cities, no sophisticated method is available to obtain the MPFs for the combined traffic loads. This study presents a novel extreme value analysis approach to predict the extreme load effects on road-rail bridges and investigate the corresponding MPFs. The core of the new approach is an unsupervised clustering algorithm based on a generalized extreme value mixture model (GEVMM), which can fit data with mixed distribution characteristics and can predict extreme values. The road-vehicle flow is represented by vehicle parameters and traffic parameters. The train flow is generated based on the distribution of passenger volume and train schedule. The 1000-day load effects on road-rail bridges with various spans are simulated and then used to predict the maximum load effects in 100 years based on the proposed method and the conventional generalized extreme value (GEV) approach, respectively. The comparison among the predicted maximum load effects and those obtained from the simulated 100-year road-rail traffic flow shows that the proposed approach is more reliable than the conventional GEV approach. Then the MPF values of the road-rail bridges are calculated from the predicted maximum load effects during a specified return period of 2000 years. The proposed method provides a valuable reference for the design and evaluation of road-rail bridges with various spans.