A Novel Acceleration-Based Approach for Monitoring the Long-Term Displacement of Bridge Cables

被引:12
|
作者
Zhang, Han [1 ]
Mao, Jianxiao [1 ]
Wang, Hao [1 ]
Zhu, Xiaojie [1 ]
Zhang, Yiming [1 ]
Gao, Hui [1 ]
Ni, Youhao [1 ]
Hai, Zong [1 ]
机构
[1] Southeast Univ, Minist Educ, Key Lab Concrete & Prestressed Concrete Struct, Nanjing 211189, Peoples R China
基金
中国国家自然科学基金;
关键词
Bridge cables; displacement estimation; acceleration-based approach; long-term; monitoring; DOUBLE INTEGRATION; INDUCED VIBRATIONS; SYSTEM; GPS; IDENTIFICATION; ACCELEROMETER; RELIABILITY; RECORDINGS;
D O I
10.1142/S0219455423500530
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The cables of the long-span bridge are usually featured as ultra-low frequency, hence making the acceleration unable to accurately capture the information, e.g. damping ratios, for assessing the cable state assessment and mitigating the excessive structural vibration. The displacement was approved to be more sensitive to the low-frequency vibration than the acceleration. However, there is still a lack of effective method to accurately monitor the long-term displacements of bridge cables using reference-free methods. To address this issue, this paper develops a novel acceleration-based approach for monitoring the long-term displacements of the cables of long-span bridges. In the monitoring scheme, recursive least squares method is utilized to conduct baseline correction in the time domain integration of acceleration. An adaptive band-pass filtering method considering cable vibration characteristics is used to eliminate noise, thus avoiding the difficulty of selecting the cut-off frequency by experience in traditional methods. A numerical test of an analytical cable model and a field experiment of the hanger of a full-scale suspension bridge are applied to the applicability and robustness of the developed method. Result shows that adaptive band-pass filter considering the vibration characteristics is suitable for estimating the displacements of the cables. The estimated displacements using the developed method agree well with the background truth in both time and frequency domains.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Long-term bridge health monitoring and performance assessment based on a Bayesian approach
    Kim, Chul-Woo
    Zhang, Yi
    Wang, Ziran
    Oshima, Yoshinobu
    Morita, Tomoaki
    STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2018, 14 (07) : 883 - 894
  • [2] Long-term monitoring of super-long stay cables on a cable-stayed bridge
    Shen, Xiang
    Ma, Ru-jin
    Ge, Chun-xi
    Hu, Xiao-hong
    WIND AND STRUCTURES, 2018, 27 (06) : 357 - 368
  • [3] Data Set from Long-Term Wind and Acceleration Monitoring of the Hardanger Bridge
    Fenerci, Aksel
    Kvale, Knut Andreas
    Petersen, Oyvind Wiig
    Ronnquist, Anders
    Oiseth, Ole
    JOURNAL OF STRUCTURAL ENGINEERING, 2021, 147 (05)
  • [4] Acceleration-based bridge weigh-in-motion
    Mohammed, Yahya M.
    Uddin, Nasim
    BRIDGE STRUCTURES, 2018, 14 (04) : 131 - 138
  • [5] Wearable acceleration-based action recognition for long-term and continuous activity analysis in construction site
    Gong, Yue
    Yang, Kanghyeok
    Seo, JoonOh
    Lee, Jin Gang
    JOURNAL OF BUILDING ENGINEERING, 2022, 52
  • [6] Bridge Condition Assessment Based on Long-term Strain Monitoring
    Sun, Limin
    Sun, Shouwang
    SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2011, 2011, 7981
  • [7] A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage
    Wang, Shuo
    OBrien, Eugene J.
    McCrum, Daniel P.
    APPLIED SCIENCES-BASEL, 2021, 11 (16):
  • [9] Baseline testing and long-term monitoring of the Lambert Road Bridge for the long-term bridge performance program
    Barr P.J.
    Petroff S.M.
    Hodson D.J.
    Thurgood T.P.
    Halling M.W.
    Journal of Civil Structural Health Monitoring, 2012, 2 (2) : 123 - 135
  • [10] Acceleration-Based Deep Learning Method for Vehicle Monitoring
    Zhu, Yanjie
    Sekiya, Hidehiko
    Okatani, Takayuki
    Yoshida, Ikumasa
    Hirano, Shuichi
    IEEE SENSORS JOURNAL, 2021, 21 (15) : 17154 - 17161