Identification of moving vehicle parameters using bridge responses and estimated bridge pavement roughness
Identification of moving vehicle parameters using bridge responses and estimated bridge pavement roughness
复制标题
DOI：
10.1016/j.engstruct.2017.10.006
复制
发表时间：
2017-12
影响因子：
5.5
通讯作者：
Haoqi Wang;T. Nagayama;Boyu Zhao;D. Su
中科院分区：
文献类型：
--
作者：
Haoqi Wang;T. Nagayama;Boyu Zhao;D. Su
过往车辆会引起桥梁变形和振动。超载车辆可能会导致桥梁疲劳损坏，甚至失效。桥梁响应与过往车辆的特性有关，特别是与车辆的自重有关。因此，桥梁动态称重系统的车辆参数估计对于评估桥梁在重复荷载作用下的状态具有重要意义。然而，传统的动态称重方法，包括在桥梁构件上安装应变计并使用已知的称重卡车进行校准，往往昂贵且耗时。本文研究了一种利用桥梁加速度响应识别移动车辆参数的方法。采用了一种基于贝叶斯理论的粒子滤波的时间域方法。在考虑车桥相互作用的情况下，利用装有传感器的车辆的车辆响应预先估计桥面平整度，并将其用于参数估计。该方法不需要校准。数值仿真结果表明，该方法对包括车辆重量在内的车辆参数估计具有较高的精度，且对观测噪声和建模误差具有较强的鲁棒性。最后，通过现场测量对该方法进行了验证。由此得到的车辆质量估计值与实测值吻合较好，证明了该方法的实用性。
Passing vehicles cause bridge deformation and vibration. Overloaded vehicles can result in fatigue damage to, or even failure of, the bridge. The bridge response is related to the properties of the passing vehicles, particularly the vehicle weight. Therefore, a bridge weigh-in-motion system for estimating vehicle parameters is important for evaluating the bridge condition under repeated load. However, traditional weigh-in-motion methods, which involve the installation of strain gauges on bridge members and calibration with known weight truck, are often costly and time-consuming. In this paper, a method for the identification of moving vehicle parameters using bridge acceleration responses is investigated. A time-domain method based on the Bayesian theory application of a particle filter is adopted. The bridge pavement roughness is estimated in advance using vehicle responses from a sensor-equipped car with consideration of vehicle-bridge interaction, and it is utilized in the parameter estimation. The method does not require the calibration. Numerical simulations demonstrate that the vehicle parameters, including the vehicle weight, are estimated with high accuracy and robustness against observation noise and modeling error. Finally, this method is validated through field measurement. The resulting estimate of vehicle mass agrees with the measured value, demonstrating the practicality of the proposed method.