Data-driven identification of structural damage under unknown seismic excitations using the energy integrals of strain signals transformed from transmissibility functions

被引:10
|
作者
Liu, Lijun [1 ]
Zhang, Xin [1 ,2 ]
Lei, Ying [1 ]
机构
[1] Xiamen Univ, Dept Civil Engn, Xiamen 361005, Peoples R China
[2] China Construction Sci & Ind Corp Ltd, Guangzhou 510030, Peoples R China
基金
中国国家自然科学基金;
关键词
Damage identification; Transmissibility function; Strain response; Energy variation; Unknown seismic excitation; QUANTIFICATION; COVARIANCE; VIBRATION;
D O I
10.1016/j.jsv.2022.117490
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
In recent years, the methodology based on the energy variations of structural dynamic strain responses has been developed. Although it is an efficient data-driven structural damage identi-fication approach, it can only be used for structures under impulse or known excitations. To overcome this limitation, an improved method is proposed in this paper for the rapid damage identification of beam-like structures using structural dynamic strain responses under unknown seismic excitations. The influence of different excitations to an intact and damaged structure, in which the intact structure is under ambient excitations and structural damage is caused by un-known seismic excitations, is eliminated via the transmissibility functions (TFs) of measured structural strain responses. Contrary to the previous TFs of structural acceleration or displace-ment responses, TFs of structural strains is more sensitive to local damage in beam-like structures. Moreover, power spectral density transmissibility functions (PSDTFs) are used instead of the traditional Fourier transform TFs. Then, through the inverse Fourier transformation in the fre-quency domain, the PSDTFs are returned to the time-domain signals to avoid the problem of selecting the proper frequency band in previous studies on identifying structural damage based on TFs in the frequency domain. Finally, structural damage can be identified based on the energy variations of the time-domain strain signals from the intact and damaged structures. Numerical examples of structural damage identification of a seven-storey planar frame and a cable-stayed bridge girder under unknown seismic excitations are provided to demonstrate the effectiveness and robustness of the improved method. Moreover, structural damage identification of a labo-ratory cable-stayed bridge tower model subjected to shaking table tests is performed to validate the good performance of the proposed method.
引用
收藏
页数:13
相关论文
共 14 条
  • [1] Data-Driven Structural Damage Identification Using DIT
    Shahidi, S. Golnaz
    Yao, Ruigen
    Chamberlain, Michael B. W.
    Nigro, Mallory B.
    Thorsen, Andrew
    Pakzad, Shamim N.
    DYNAMICS OF CIVIL STRUCTURES, VOL 2, 2015, : 219 - 226
  • [2] Baseline-free damage localization of structures under unknown seismic excitations based on strain transmissibility and wavelet transform of strain mode
    Yang, Xiongjun
    Lei, Ying
    Liu, Lijun
    Mi, Jianan
    Liu, Weifeng
    STRUCTURES, 2024, 61
  • [3] Structural Damage Localization under Unknown Seismic Excitation Based on Mahalanobis Squared Distance of Strain Transmissibility Function
    Liu, Lijun
    Zhang, Xin
    Lei, Ying
    Zheng, Zhupeng
    APPLIED SCIENCES-BASEL, 2022, 12 (06):
  • [4] Damage detection of bridge structures under unknown seismic excitations using support vector machine based on transmissibility function and wavelet packet energy
    Liu, Lijun
    Mi, Jianan
    Zhang, Yixiao
    Lei, Ying
    SMART STRUCTURES AND SYSTEMS, 2021, 27 (02) : 257 - 266
  • [5] Structural Damage Detection Using Auto/Cross-Correlation Functions Under Multiple Unknown Excitations
    Ni, Pinghe
    Xia, Yong
    Law, Siu-Seong
    Zhu, Songye
    INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2014, 14 (05)
  • [6] Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data
    Lei, Ying
    Zhang, Yixiao
    Mi, Jianan
    Liu, Weifeng
    Liu, Lijun
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2021, 20 (04): : 1583 - 1596
  • [7] Distributions of fatigue damage from data-driven strain prediction using Gaussian process regression
    Gibson, Samuel J.
    Rogers, Timothy J.
    Cross, Elizabeth J.
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2023, 22 (05): : 3065 - 3076
  • [8] Data driven structural damage assessment using phase space embedding and Koopman operator under stochastic excitations
    Peng, Zhen
    Li, Jun
    Hao, Hong
    ENGINEERING STRUCTURES, 2022, 255
  • [9] A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations
    Fathnejat, Hamed
    Ahmadi-Nedushan, Behrouz
    Hosseininejad, Sahand
    Noori, Mohammad
    Altabey, Wael A.
    ENGINEERING STRUCTURES, 2023, 276
  • [10] Robust data-driven online learning algorithm for precise structural damage localization using stochastic subspace identification
    Banerjee, Subhajit
    Saravanan, T. Jothi
    MEASUREMENT, 2025, 244