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Abstract

To efficiently and accurately identify the moving load parameters on bridge structures, this paper proposed a layered identification method for moving loads on bridges using BP neural networks based on the whale optimization algorithm (WOA), and built a moving load parameter identification model based on WOA-BP neural networks. By adopting this moving load identification model, step-by-step coupling identification was conducted on the action lane, speed, and load capacity of vehicles on the bridge. Additionally, by taking a prestressed simply supported box girder bridge as the research project, the applicability of the proposed method and neural network identification model was verified via dynamic response measurement data under the action of random traffic flow and the combination of video data of vehicles driving on the bridge. The results reveal that the proposed method for identifying moving load parameters features high identification accuracy, fast convergence speed, strong robustness and noise resistance, and can accurately identify bridges' moving load parameters.

Publication Date

1-18-2024

DOI

10.14048/j.issn.1671-2579.2023.06.014

First Page

85

Last Page

93

Submission Date

March 2025

Reference

[1] 劳家荣, 谭皓, 鲁乃唯. 基于车辆WIM监测数据的大跨桥梁作用效应极值分析[J]. 中外公路, 2020, 40(3): 114-118. LAO Jiarong, TAN Hao, LU Naiwei. Extreme value analysis of long-span bridge action effect based on vehicle WIM monitoring data [J]. Journal of China & Foreign Highway, 2020, 40(3): 114-118. [2] LYDON M, TAYLOR S E, ROBINSON D, et al. Recent developments in bridge weigh in motion (B-WIM)[J]. Journal of Civil [3] 段卫党, 邓泽城, 上官煜, 等. 小箱梁桥参数对大件车载效应的影响及快速评估[J]. 中外公路, 2022, 42(5): 132-137. DUAN Weidang, DENG Zecheng, SHANGGUAN Yu, et al. The influence of small box girder bridge parameters on the vehicle effect of large parts and its rapid evaluation [J]. Journal of China & Foreign Highway, 2022, 42(5): 132-137. [4] YU Y, CAI C S, DENG L. State-of-the-art review on bridge weigh-in-motion technology[J]. Advances in Structural Engineering, 2016, 19(9): 1514-1530. [5] 王宁波, 任伟新, 李苗. 基于影响线的桥梁移动荷载识别[J]. 振动与冲击, 2013, 32(3): 129-133. WANG Ningbo, REN Weixin, LI Miao. Moving load identification of a bridge based on influence line[J]. Journal of Vibration and Shock, 2013, 32(3): 129-133. [6] 王宁波, 任伟新, 万华平. 基于动应变的桥梁动态称重及其优化算法[J]. 振动与冲击, 2013, 32(4): 116-120. WANG Ningbo, REN Weixin, WAN Huaping. Bridge weigh-in-motion and its optimization algorithm based on dynamic strain[J]. Journal of Vibration and Shock, 2013, 32(4): 116-120. [7] 安家禾, 赵华, 马鹏飞, 等. 应用于OSD的两种轴重识别算法对比分析[J]. 中外公路, 2022, 42(4): 132-138. AN Jiahe, ZHAO Hua, MA Pengfei, et al. Comparative analysis of two axle load identification algorithms applied to OSD [J]. Journal of China & Foreign Highway, 2022, 42(4): 132-138. [8] 陈震, 余岭. 基于截断GSVD方法的桥梁移动荷载识别[J]. 振动与冲击, 2014, 33(10): 97-100,130. CHEN Zhen, YU Ling. Identification of dynamic axle loads on a bridge based on truncated generalized singular value decomposition[J]. Journal of Vibration and Shock, 2014, 33(10): 97-100,130. [9] 陈震，余岭.基于TGSVD的桥梁移动荷载识别及正则化矩阵选取[J].振动·测试与诊断，2015，35(1)：24‑29，184. CHEN Zhen, YU Ling. Identification of moving loads on bridges and selection of regularization matrices based on TGSVD [J]. Journal of Vibration, Measurement & Diagnosis, 2015, 35(1): 24-29, 184 [10] 张超东, 黎剑安, 张浩. 基于增秩Kalman滤波的移动车辆荷载在线识别[J]. 振动与冲击, 2022, 41(2): 87-95. ZHANG Chaodong, LI Jian’an, ZHANG Hao. Augmented Kalman filter based moving vehicle loads online identification[J]. Journal of Vibration and Shock, 2022, 41(2): 87-95. [11] CHEN Z, CHAN T H T, NGUYEN A, et al. Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm[J]. Mechanical Systems and Signal Processing, 2019, 128: 479-496. [12] 邹谭.基于桥梁结构响应的车辆荷载识别方法研究[D].重庆：重庆交通大学，2019. ZOU Tan. Study on vehicle load identification method based on bridge structural response [D]. Chongqing: Chongqing Jiaotong University, 2019. [13] XU B, WU Z S, CHEN G D, et al. Direct identification of structural parameters from dynamic responses with neural networks[J]. Engineering Applications of Artificial Intelligence, 2004, 17(8): 931-943. [14] 杨慧, 李奉阁, 闫维明, 等. 基于ANN和动应变的梁桥移动荷载识别及试验[J]. 振动 测试与诊断, 2018, 38(2): 305-313,419. YANG Hui, LI Fengge, YAN Weiming, et al. Moving load identification and experimental verification of beam bridge based on dynamic strain and ANN[J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(2): 305-313,419. [15] 李忠献, 陈锋, 王波. 基于BP神经网络的桥上移动荷载分阶段识别方法[J]. 工程力学, 2008, 25(9): 85-92. LI Zhongxian, CHEN Feng, WANG Bo. A bp neural network-based stage identification method for moving loads on bridges[J]. Engineering Mechanics, 2008, 25(9): 85-92. [16] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67. [17] 孔烜, 张杰, 邓露, 等. 基于机器视觉的车辆检测与参数识别研究进展[J]. 中国公路学报, 2021, 34(4): 13-30. KONG Xuan, ZHANG Jie, DENG Lu, et al. Research advances on vehicle parameter identification based on machine vision[J]. China Journal of Highway and Transport, 2021, 34(4): 13-30. [18] 周毅, 徐柏龄. 神经网络中的正交设计法研究[J]. 南京大学学报(自然科学版), 2001, 37(1): 72-78. ZHOU Yi, XU Boling. Orthogonal method for training neural networks[J]. Journal of Naijing University (Natural Sciences), 2001, 37(1): 72-78. [19] DOWLING J, OBRIEN E J, GONZÁLEZ A. Adaptation of Cross Entropy optimisation to a dynamic Bridge WIM calibration problem[J]. Engineering Structures, 2012, 44: 13-22. [20] 宋宇. 基于动应变数据的桥梁移动荷载识别研究[D]. 武汉: 武汉理工大学, 2014. SONG Yu. Research on bridge moving load identification based on dynamic strain data. Wuhan: Wuhan University of Technology, 2014. [21] LI C P, PENG T H, ZHU Y M, et al. Noise reduction method of shearer’s cutting sound signal under strong background noise[J]. Measurement and Control, 2022, 55(7/8): 783-794.

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