Integrating bridge influence surface and computer vision for bridge weigh-in-motion in complicated traffic scenarios

Complicated traffic scenarios, including random change in the speed and lane of vehicles, as well as the simultaneous presence of multiple vehicles on the bridge, are the main obstacles that prevent bridge weigh-in-motion (BWIM) technique from reliable and accurate application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method, which integrates the deep-learning-based computer vision technique and bridge influence surface theory. In this study, bridge strains and traffic videos are recorded synchronously as the data source of BWIM. The computer vision technique is employed to detect and track vehicles and corresponding axles from traffic videos so that spatio-temporal paths of vehicle loads on the bridge can be obtained, enabling the usage of the bridge strain influence surface (SIS) for BWIM purposes. Then the SIS of the bridge structure is calibrated based on the time-synchronized strain signals and vehicle paths. After the SIS is calibrated, the axle weight (AW) and gross vehicle weight (GVW) can be identified by integrating the SIS, time-synchronized bridge strain, and vehicle paths. For illustration and verification, the proposed method is applied to identify AW and GVW in scale model experiments, in which the vehicle-bridge system is designed with high fidelity, and various complicated traffic scenarios are simulated. Results confirm that the proposed method contributes to improving the existing BWIM technique with respect to complicated traffic scenarios.

Automated summary

This paper presents a novel bridge weigh-in-motion (BWIM) method that integrates deep-learning-based computer vision technique and bridge influence surface theory to tackle the complicated traffic problems. The computer vision technique is used to detect and track vehicles and axles, obtaining the spatio-temporal paths of vehicle loads on the bridge. The proposed method improves the existing BWIM technique with respect to complicated traffic scenarios.