Multi-lane factor (MLF) is a probability reduction reflecting unfavorable traffic loads over multiple lanes acting simultaneously on the most adverse position of a bridge. It is one of the key components of traffic load models for bridges. The most recent research established a multi-coefficient MLF model that clearly illustrated the lane load disparity and the probability reduction of their simultaneous actions. However, it used the block maxima (BM) method for extreme value modeling, which requires a large amount of traffic data. This study aims to adopt the peaks-over-threshold (POT) method to obtain more information from short-term traffic data and model the extreme coincident lane load effects (LLEs) for multi-coefficient MLF calibration. First, the multi-coefficient MLF model was reviewed. Thereafter, the bivariate POT method for coincident LLEs modeling using generalized Pareto distribution was proposed and formulated. Critical issues such as bivariate threshold selection and parameter estimation were addressed. Numerical examples were demonstrated to verify and validate the approach. Finally, the proposed approach was applied for calibrating the MLF of an experimental site with four traffic lanes. The results indicated that the coincident LLEs modeling using the POT approach was accurate and more effective than using the BM method when applied to limited data. The calibrated MLFs from the experimental site effectively revealed the lane load disparity of traffic loads over multiple lanes, which is not involved in the traffic load models of current bridge design specifications. Furthermore, the influence of other problems such as weight restriction on coincident LLEs modeling and MLF calibration were discussed. The proposed technique provides a sound approach for multi-coefficient MLF calibration of bridge assessment with short-term site-specific traffic data.
Evaluation of the extreme traffic load effects on the Forth Road Bridge using image analysis of traffic data
