Research on Traffic-video-aided Bridge Weigh-in-motion Approach

In order to improve the existing bridge weigh-in-motion (BWIM) technique, a novel traffic-video-aided BWIM methodology is proposed. First, an object detection method based on the deep neural network and a coordinate transformation method is introduced. They were used to detect and locate vehicles and axles on the bridge in real time. Then, a bridge strain decomposition method and a method to identify the strain influence surface of bridge structures were proposed to establish the mapping relationship between axle weight, axle position, and vehicle-induced static bridge strain. Subsequently, a method that comprehensively uses temporal and spatial redundancy information to identify the axle weight and gross weight of vehicles was proposed. The method first constructed an overdetermined equation set of influence surface and used the least square method to solve the equation set then to obtain the axle weight and gross weight of vehicles on the bridge. The framework of the novel BWIM approach was summarized and verified through scale model experiments and field tests on a continuous large box girder bridge. The experiments included various traffic scenarios such as single vehicle, double vehicle, following vehicle, parallel vehicle, straight path, curved path, constant speed, and variant speed. Results of the model experiments show that the mean and the standard deviation of the error for identifying gross vehicle weight (GVW)is -2.02% and 4.77%.The mean and the standard deviation of the error for identifying axle weight (AW) is 4.77% and 17.50%.Results of the field tests show that the mean and the standard deviation of the error for identifying GVW is 0.21% and 153%. The mean and the standard deviation of the error for identifying AW are -3.59% and 42.67%. In addition, the method can also be used to identify the traffic information such as the number, type, number of axles, real-time position, trajectory, and speed of vehicles on the bridge. © 2021, Editorial Department of China Journal of Highway and Transport. All right reserved.