Abstract：In order to give full play to function of a monitoring system and realize vehicle load identification of large bridges, the relevant theories and methods of bridge weight in motion (B-WIM) technology based on health monitoring system (HMS) were studied by combining HMS and B-WIM. Based on an actual cable-stayed bridge and its HMS, the monitored operating cable force was taken as the observation parameter, and the cable force time-history characteristic analysis and neural network method were used to recognize vehicle speed and weight. Firstly, the operating cable force was processed and analyzed with wavelet and EMD, and the vehicle response component of the cable force was separated from constant load, temperature response and random interference parts. Then, the vehicle speed was pre-estimated with the vehicle response’s peak sharpness of the single-cable force, and the vehicle speed was calculated by matching vehicle response peak values of multi-cable forces. Secondly, a BP network model for vehicle weight recognition was established based on multi-cable force response. According to the joint distribution model for vehicle speed and weight of similar highways, a fleet sample was constructed for vehicle-bridge coupled analysis. Cable force responses were extracted to build
2 916 sets of data samples for network training and testing, and realize vehicle weight recognition network training with higher accuracy. Finally, using 24-hour continuous cable force monitoring data of an actual cable-stayed bridge, the vehicle speed and weight recognition method described above was applied and tested in practical engineering to recognize 463 vehicles with weight of more than 50 kN. The statistical analysis for the recognized results showed that the recognized vehicle speed and weight distributions and their joint distribution agree better with real ones; stayed cables has the feature of wide distribution in a full bridge space, cable force is also the response measured necessarily of the monitoring system and has good sensitivity to vehicle, taking cable force as the observation parameter to realize the recognition of vehicle weight and speed is feasible; this method’s data processing and vehicle identification process are easy to realize online operation and automation.
孙宗光1，陈一飞2. 基于斜拉桥索力监测的在线车速车重识别[J]. 振动与冲击, 2020, 39(17): 134-141.
SUN Zongguang1, CHEN Yifei2. Online vehicle speed and weight recognition based on cable force monitoring of cable-stayed bridge. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(17): 134-141.
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