Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.

中文翻译：

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使用桥梁测量的基于小波的车轴最优识别

桥梁健康监测系统通常需要准确的车辆配置（车速、轴数和轴间距），并且是桥梁动态称重 (BWIM) 系统的先决条件。使用“道路上没有任何东西”的原则，这些数据是使用轴检测传感器（通常是应变计）找到的，这些传感器放置在桥底的特定位置。为了改进测量信号中的轴检测，本文提出了小波变换和具有相关因子的香农熵。所提出的方法首先通过数值模拟进行验证，然后在两个现场试验中进行测试。研究了所提出方法的保真度，包括测量中的噪声、多重存在、不同的车辆速度、不同类型的车辆和实际交通流量。