+高级检索
适用于随机车流的二维桥梁动态称重应用
DOI:
作者:
作者单位:

作者简介:

通讯作者:

基金项目:


A 2-Dimension Bridge Weigh-in-Motion System under Random Traffic
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    为解决桥梁动态称重(Bridge weigh-in-motion,BWIM)中多车共存时的轴、总重识 别难题,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的情况,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 . 通过标定试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,通过三次随机车流现场测试对 2D-BWIM 算法展开验 证 . 结果表明,当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 结果显示,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%)

    Abstract:

    To identify the axle/total weight in the case of multiple vehicles,this paper proposes a twodimensional(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 correspond? ing 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 car? ried 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 compar? ing 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.

    参考文献
    相似文献
    引证文献
文章指标
  • PDF下载次数:
  • HTML阅读次数:
  • 摘要点击次数:
  • 引用次数:
引用本文

谭承君 ,赵华 ,张斌 ,郭泓捷 .适用于随机车流的二维桥梁动态称重应用[J].湖南大学学报:自然科学版,2022,49(5):111~119

复制
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-06-06
  • 出版日期:
作者稿件一经被我刊录用,如无特别声明,即视作同意授予我刊论文整体的全部复制传播的权利,包括但不限于复制权、发行权、信息网络传播权、广播权、表演权、翻译权、汇编权、改编权等著作使用权转让给我刊,我刊有权根据工作需要,允许合作的数据库、新媒体平台及其他数字平台进行数字传播和国际传播等。特此声明。
关闭