A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.

实时车辆监控对于有效的桥梁维护和交通管理至关重要，因为超载的车辆会损坏桥梁，在某些极端情况下，这将直接导致桥梁故障。桥梁动态称重（BWIM）系统是一种高性能且具有成本效益的技术，已广泛用于监视高速公路上的车辆速度和重量。但是，动态影响和数据噪声可能会在车辆通过桥梁期间和之后立即对桥梁响应产生不利影响。快速傅立叶变换（FFT）方法可以显着地净化从传感器或传感器接收到的结构响应（动态应变），该方法被用于轴计数，检测和轴称量技术中。为了进一步提高BWIM系统的准确性，原位实验结果表明，将FFT滤波器处理后的信号远优于原始信号，将FFT方法引入BWIM系统可以显着提高车轴检测的效率和准确性。此外，应通过将特定车道的计算出的平均IL单独用于该车道的车辆重量计算，来考虑对桥梁的横向载荷分布影响。