Fully automated and non-contact force identification of bridge cables using microwave remote sensing

被引:16
作者
Weng, Jinghang [1 ]
Chen, Lin [1 ,6 ]
Sun, Limin [1 ,2 ,3 ,6 ]
Zou, Yiqing [4 ]
Liu, Zhanhang [1 ]
Guo, Hui [5 ]
机构
[1] Tongji Univ, Dept Bridge Engn, Shanghai 200092, Peoples R China
[2] Tongji Univ, Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
[3] Shanghai Qi Zhi Inst, Shanghai 200092, Peoples R China
[4] OVM Machinery Ltd, Liuzhou 545006, Peoples R China
[5] China Acad Railway Sci Corp Ltd, Railway Engn Res Inst, Beijing 100081, Peoples R China
[6] Tongji Univ, Dept Bridge Engn, 1239 Siping Rd, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
Cable force; Fast sieve method; Weighted hash voting; Microwave radar; Automated force identification; TENSION DETERMINATION; STAYED BRIDGES; VIBRATION; FORMULAS;
D O I
10.1016/j.measurement.2023.112508
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Efficient force identification of bridge cables is important to the performance evaluation of cable-supported bridges. For this purpose, sensors are conventionally installed on the cables to record vibration responses, which is nevertheless time-consuming and labor-intensive. This study develops a fully automated and noncontact cable force identification method using microwave radar sensing, enabling forces of a group of cables to be identified simultaneously and thus largely improving the efficiency. The fast sieve method with a linear time complexity is proposed to find the local maxima of the Fourier spectrum of the displacement measurements. Subsequently, the weighted hash voting is presented to identify the mode orders of the picked frequencies. An appropriate cable vibration model can then be used to compute the tension force. The proposed method has been validated in cable force identification of a newly constructed railway cable-stayed bridge during loading tests. Cable force variations in the loading tests are discussed.
引用
收藏
页数:15
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共 42 条
  • [31] A wireless smart sensor network for automated monitoring of cable tension
    Sim, Sung-Han
    Li, Jian
    Jo, Hongki
    Park, Jong-Woong
    Cho, Soojin
    Spencer, Billie F., Jr.
    Jung, Hyung-Jo
    [J]. SMART MATERIALS AND STRUCTURES, 2014, 23 (02)
  • [32] Stay cable vibration mitigation: A review
    Sun, Limin
    Chen, Lin
    Huang, Hongwei
    [J]. ADVANCES IN STRUCTURAL ENGINEERING, 2022, 25 (16) : 3368 - 3404
  • [33] Bridge stay cable condition assessment using vibration measurement techniques
    Tabatabai, H
    Mehrabi, AB
    Yen, WP
    [J]. STRUCTURAL MATERIALS TECHNOLOGY III: AN NDT CONFERENCE, 1998, 3400 : 194 - 204
  • [34] Noncontact cable force estimation with unmanned aerial vehicle and computer vision
    Tian, Yongding
    Zhang, Cheng
    Jiang, Shang
    Zhang, Jian
    Duan, Wenhui
    [J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2021, 36 (01) : 73 - 88
  • [35] Tension determination for suspenders of arch bridge based on multiple vibration measurements concentrated at one end
    Wu, Wen-Hwa
    Chen, Chien-Chou
    Chen, Yu-Chuan
    Lai, Gwolong
    Huang, Chun-Ming
    [J]. MEASUREMENT, 2018, 123 : 254 - 269
  • [36] Mode shape-aided cable force determination using digital image correlation
    Yan, Banfu
    Li, Derui
    Chen, Wenbing
    Deng, Lu
    Jiang, Xiaomo
    [J]. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021, 20 (05): : 2430 - 2445
  • [37] Tension Force Estimation of Cables with Two Intermediate Supports
    Yan, Banfu
    Chen, Wenbing
    Dong, You
    Jiang, Xiaomo
    [J]. INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2020, 20 (03)
  • [38] Mode shape-aided tension force estimation of cable with arbitrary boundary conditions
    Yan, Banfu
    Chen, Wenbing
    Yu, Jiayong
    Jiang, Xiaomo
    [J]. JOURNAL OF SOUND AND VIBRATION, 2019, 440 : 315 - 331
  • [39] Estimation of Cable Tension Force Independent of Complex Boundary Conditions
    Yan, Banfu
    Yu, Jiayong
    Soliman, Mohamed
    [J]. JOURNAL OF ENGINEERING MECHANICS, 2015, 141 (01)
  • [40] Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach
    Zhan, Shaodong
    Li, Zhi
    Hu, Jianmin
    Liang, Yiping
    Zhang, Guanglie
    [J]. SENSORS, 2018, 18 (12)