Abstract

The static bridge weigh-in-motion (BWIM) systems are mostly based on strain measurements and are particularly suited for stiff short-span bridges. Recently, the BWIM systems based on acceleration measurements are developed for long-span bridges because of the portability and low-cost of accelerometers as compared to strain gauges. Although, these BWIM systems can estimate the gross vehicle weights (GVWs) with high accuracy, but they fail to identify the weights of individual axles accurately especially for vehicles with closely spaced axles. In this paper, an iterative linear optimization problem (ILOP) was proposed to accurately identify the individual axle weights and GVWs of vehicles traversing a bridge. A BWIM system consisting of only microelectromechanical system (MEMS) accelerometers was employed in an in-service steel girder bridge having multiple lanes. The proposed method used the bridge displacement responses as the measured responses which were determined from the recorded acceleration data. The information about the vehicle speed, number of axles and axle spacings were obtained by identifying the peaks in the recorded acceleration data. The effectiveness and accuracy of the proposed method were demonstrated through field tests using the four-axle test vehicles with closely spaced axles. The results showed that the axle weights of vehicles with closely spaced axles could be identified with much better accuracy by the proposed method as compared to classic BWIM systems which are based on Moses’ original algorithm.

摘要

静态桥梁动态称重 (BWIM) 系统主要基于应变测量，特别适用于刚性短跨桥梁。最近，由于加速度计与应变计相比具有便携性和低成本，因此为大跨度桥梁开发了基于加速度测量的 BWIM 系统。虽然这些 BWIM 系统可以高精度地估计车辆总重量 (GVW)，但它们无法准确识别单个车轴的重量，尤其是对于车轴间距很近的车辆。在本文中，提出了迭代线性优化问题 (ILOP)，以准确识别穿过桥梁的车辆的各个轴重和 GVW。一个仅由微机电系统 (MEMS) 加速度计组成的 BWIM 系统被用于具有多个车道的在役钢梁桥。所提出的方法使用桥梁位移响应作为从记录的加速度数据确定的测量响应。通过识别记录的加速度数据中的峰值，获得有关车辆速度、轴数和轴距的信息。通过使用具有紧密间隔的轴的四轴试验车辆的现场测试证明了所提出方法的有效性和准确性。结果表明，与基于摩西原始算法的经典 BWIM 系统相比，所提出的方法可以更准确地识别具有紧密轴距的车辆的轴重。所提出的方法使用桥梁位移响应作为从记录的加速度数据确定的测量响应。通过识别记录的加速度数据中的峰值，获得有关车辆速度、轴数和轴距的信息。通过使用具有紧密间隔的轴的四轴试验车辆的现场测试证明了所提出方法的有效性和准确性。结果表明，与基于摩西原始算法的经典 BWIM 系统相比，所提出的方法可以更准确地识别具有紧密轴距的车辆的轴重。所提出的方法使用桥梁位移响应作为从记录的加速度数据确定的测量响应。通过识别记录的加速度数据中的峰值，获得有关车辆速度、轴数和轴距的信息。通过使用具有紧密间隔的轴的四轴试验车辆的现场测试证明了所提出方法的有效性和准确性。结果表明，与基于摩西原始算法的经典 BWIM 系统相比，所提出的方法可以更准确地识别具有紧密轴距的车辆的轴重。通过识别记录的加速度数据中的峰值来获得轴数和轴距。通过使用具有紧密间隔的轴的四轴试验车辆的现场测试证明了所提出方法的有效性和准确性。结果表明，与基于摩西原始算法的经典 BWIM 系统相比，所提出的方法可以更准确地识别具有紧密轴距的车辆的轴重。通过识别记录的加速度数据中的峰值来获得轴数和轴距。通过使用具有紧密间隔的轴的四轴试验车辆的现场测试证明了所提出方法的有效性和准确性。结果表明，与基于摩西原始算法的经典 BWIM 系统相比，所提出的方法可以更准确地识别具有紧密轴距的车辆的轴重。