Jiaxin Yang

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.