Dead load based damage identification method for long-term structural health monitoring

被引:3
|
作者
Hu, Xiaofeng
Shenton, Harry W., III [1 ]
机构
[1] Edwards & Kelcey, W Chester, PA 19380 USA
[2] Univ Delaware, Newark, DE 19716 USA
关键词
damage identification; dead load; frame; finite element; health monitoring; genetic algorithm; optimization; static; strain;
D O I
10.1177/1045389X06070599
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A novel damage identification procedure is presented that is ideally suited for long-term structural health monitoring of large civil structures. The procedure is based on the redistribution of dead load stress that occurs in a structure when damage takes place. The damaged structure is modeled using finite elements, in which the damage is represented by a section of reduced flexural rigidity. Forward analyses are first presented to show how the dead load is redistributed for different damage scenarios. The inverse damage identification problem is set up as a constrained optimization problem and solved using a real coded genetic algorithm. The proposed method correctly identified damage for a wide range of locations and damage severities. Results show that damage is difficult to identify when it is close to the inflection points of the structure, where the dead load strain is zero, and when the damage is not located between two sensors. The effect of measurement error is investigated.
引用
下载
收藏
页码:923 / 938
页数:16
相关论文
共 50 条
  • [41] A novel recursive stochastic subspace identification algorithm with its application in long-term structural health monitoring of office buildings
    Wu, Wen-Hwa
    Jhou, Jhe-Wei
    Chen, Chien-Chou
    Lai, Gwolong
    SMART STRUCTURES AND SYSTEMS, 2019, 24 (04) : 459 - 474
  • [42] Composite and Monolithic DFOS Sensors for Load Tests and Long-Term Structural Monitoring of Road Infrastructure
    Sienko, Rafal
    Bednarski, Lukasz
    Howiacki, Tomasz
    Zuziak, Katarzyna
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 1, 2023, 253 : 595 - 605
  • [43] Long-term success by Udder health monitoring
    Kroemker, Volker
    TIERAERZTLICHE PRAXIS AUSGABE GROSSTIERE NUTZTIERE, 2011, 39 (02): : 69 - 69
  • [44] Neural network method based on a new damage signature for structural health monitoring
    Yuan, SF
    Wang, L
    Peng, G
    THIN-WALLED STRUCTURES, 2005, 43 (04) : 553 - 563
  • [45] A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data
    Ni, Y. Q.
    Wang, Y. W.
    Zhang, C.
    ENGINEERING STRUCTURES, 2020, 212
  • [46] Iterative damage index method for structural health monitoring
    You, Taesun
    Gardoni, Paolo
    Hurlebaus, Stefan
    STRUCTURAL MONITORING AND MAINTENANCE, 2014, 1 (01): : 89 - 110
  • [47] The research of soft yoke single point mooring tower system damage identification based on long-term monitoring data
    Tang, Da
    Zeng, Xianpeng
    Wang, Deyu
    Wu, Wenhua
    Wang, Yanlin
    Yue, Qianjin
    Wang, Bingsen
    Xie, Bin
    Wang, Shisheng
    Feng, Jiaguo
    APPLIED OCEAN RESEARCH, 2018, 76 : 139 - 147
  • [48] A long-term static monitoring experiment on RC beams: damage identification under environmental effect
    del Grosso, Andrea
    Lanata, Francesca
    STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2014, 10 (07) : 911 - 920
  • [49] A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data
    Hey Leung, Michael Siu
    Corcoran, Joseph
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2022, 21 (04): : 1608 - 1625
  • [50] Bridge instrumentation for long term structural health monitoring
    Sanayei, M.
    Sipple, J. D.
    Phelps, J. E.
    Santini-Bell, E.
    Lefebvre, P. J.
    Brenner, B. R.
    BRIDGE MAINTENANCE, SAFETY, MANAGEMENT AND LIFE-CYCLE OPTIMIZATION, 2010, : 835 - 842