LAUSR

Since the high randomness feature of traffic flows, it is challenging to be aware of the spatial-temporal characteristics of dynamic vehicle loads on a long-span bridge. Compared with conventional measuring approaches, computer vision-based techniques have competitive advantages in the measurement of factual vehicle loads parameters, including the passing time, velocity, vehicle type, and corresponding locations on the bridge at each timestamp. In this paper, a vision-based framework for the identifications of dynamic vehicle loads on long-span bridges is proposed. To be specific, the framework consists of two major parts. The first part is the extraction of spatial-temporal factors of vehicles, including the detection of vehicles and the estimation of tracklets. Here, Single Shot Multi-Box Detection (SSD) architecture with MobileNets is utilized for detecting vehicles, while the data association technique with Kalman filter is applied on detection results for generating tracklets. Then, in the second part, based on the detection and tracking results, a Multi-layer Monte Carlo (MLMC) model is developed for axle-load intensities inference and estimation. The learned model is utilized to infer the dynamic load of vehicles from the detection results and generated tracklets. The proposed methodology is validated via an experiment of a case study on the Jiangyin bridge, Jiangsu Province, China, and several spatial-temporal distributions of vehicle loads are specifically analyzed and discussed. Experimental results indicate our proposed framework is a feasible and reliable solution to predict and identify dynamic vehicle loads on long-span bridges, without the assistance of the deck-embedded Weigh-in-Motion (WIM) system.