适用于随机车流的二维桥梁动态称重应用研究
A 2-Dimension Bridge Weigh-In-Motion System Under Random Traffic
投稿时间:2021-05-26  修订日期:2021-09-16
DOI:
中文关键词:  桥梁工程  桥梁动态称重  随机车流  Moses算法  横向分布系数  影响线  
英文关键词:Bridge Engineering  BWIM (bridge weigh-in-motion)  Random Traffic  Moses’ algorithm  Transverse Distribution Factors  Influence Line
基金项目:国家自然科学基金项目,湖南省重点领域研发计划项目,中国博士后科学基金,湖南省科技创新计划资助
作者单位邮编
谭承君 湖南大学土木工程学院 410082
赵华 湖南大学土木工程学院 410082
张斌 湖南大学土木工程学院 
郭泓捷 湖南大学土木工程学院 
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中文摘要:
      为了解决桥梁动态称重(Bridge weigh-in-motion:BWIM)中多车共存时的轴、总重识别难题,本文基于桥梁的二维(2D)结构特性,考虑车辆的横向位置,提出了一种2D-BWIM算法。该算法仅利用移动车辆所在车道下方的梁底响应信号以及结构梁底影响线来计算轴重,利用横向分布系数的概念将轴重分配至各片梁上。桥梁每片梁底的实际结构影响线通过标定试验获得。针对多辆车同时经过桥梁时,2D-BWIM算法提出了一种迭代方法来识别多车道上每辆车的轴、总重。该迭代方法基于一种假设,即单辆车过桥时的梁底响应按照横向分布系数成比例缩放假设。本文通过标定实验分析了这种假设的实际误差,结果表明,其绝对误差非常小,对后续车辆轴重及总重识别影响甚小。随后,本文通过三次随机车流现场测试对2D-BWIM算法展开验证。其结果表明,针对单辆车经过目标桥梁时,相较于BWIM中传统Moses算法,2D-BWIM算法考虑了车辆的实时横向位置,因而能够显著提高车辆轴重及总重识别精度。三次随机车流试验结果的单辆车过桥事件中(Moses、2D-BWIM)算法的总重识别误差平均值及方差分别为(7.9%、3.1%)和(13.5%、4.8%)。此外,三次随机车流试验中多车过桥事件(Moses、2D-BWIM)算法的轴重识别误差平均值及方差分别为(7.34%,1.53%)和(26.33%,3.12%)。
英文摘要:
      To identify the axle/total weight in the case of multiple vehicles, this paper proposes a two-dimensional (2D)-BWIM algorithm considering the lateral position of passing vehicles. In this algorithm, only the responses of girders underneath the traveling lane are adopted to calculate the axle/total weight via using the concept of influence line. Considering the bridge’s 2D behavior, the axle weight is distributed on each girder based on the transverse distribution factors. The influence line of each girder was obtained through a calibration test with known vehicle information. In this study, an iterative method is used to identify the axle/total weight of passing vehicles in multiple vehicles present. This iterative method gives an assumption that the response of each girder is proportional to the transverse distribution factor when a single-vehicle crosses the bridge. Using the assumption, the corresponding error is calculated based on the calibration test, and the results show that the absolute error is very small and will not affect the accuracy of axle/total weight identification later. Then, three field tests under random traffic were carried out to validate the proposed 2D-BWIM algorithm. For a single-vehicle passing over the bridge, the results show that the 2D-BWIM algorithm can significantly improve the accuracy of vehicle axle/total weight recognition comparing to the traditional Moses algorithm. In this case, for the 2D-BWIM algorithm, the average and variance of errors in total weight identification are 3.1% and 4.8%, respectively. While for traditional Moses’ algorithm, errors of those are 7.9% and 13.5%, respectively. For multiple vehicles presents, the average and variance of errors in axle weight identification of (Moses, 2D-BWIM) algorithms are (7.34%, 1.53%) and (26.33%, 3.12%), respectively.
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