Accurate traffic conditions and their development regularity can help in the management of future traffic demand and bridge design. This study proposes a bridge weigh-in-motion method for identifying the vehicle parameters of motorway bridges considering random traffic flow and capitalizes on the advantages of long-gauge fibre Bragg grating sensors. A bridge-vehicle coupling system is numerically simulated, a 1:10 indoor experiment is used to verify the proposed method, and the vehicle parameters (e.g., traffic lane, vehicle spacing, vehicle axle weight, and speed) are identified with high accuracy. The effects of the road surface conditions, vehicle numbers, lateral positions and accelerations on the proposed method are investigated, and only find that the lateral positions are obviously related while and others have almost no influence on the method. In addition, the accuracy in axle number and weight identification is not high when the gauge length is smaller than vehicle wheelbases.

准确的交通状况及其发展规律有助于管理未来的交通需求和桥梁设计。本研究提出了一种桥梁动态称重方法，用于识别考虑随机交通流的高速公路桥梁车辆参数，并利用长规格光纤布拉格光栅传感器的优势。对桥车耦合系统进行了数值模拟，通过1:10室内实验对提出的方法进行了验证，对车辆参数（如行车道、车距、车轴重量、车速等）进行了高精度识别。研究了路面条件、车辆数量、横向位置和加速度对所提出方法的影响，并且只发现横向位置有明显的相关性，而其他对方法几乎没有影响。此外，当标距小于车辆轴距时，轴号和重量识别的准确性不高。