Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework

被引:47
|
作者
Yang, J. [1 ]
Lam, H. F. [1 ]
Hu, J. [1 ]
机构
[1] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Hong Kong, Peoples R China
关键词
Ambient vibration test; Bayesian modal identification; Bayesian model updating; Markov chain Monte Carlo simulation; FFT METHOD; UNCERTAINTIES; SYSTEM;
D O I
10.1142/S0219455415400246
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently evaluate the posterior marginal PDFs of the uncertain parameters without calculating high-dimension numerical integration, which provides posterior uncertainties for the target systems.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Bayesian model updating in an active Kriging-based metamodeling framework
    Sengupta, Partha
    Chakraborty, Subrata
    APPLIED MATHEMATICAL MODELLING, 2025, 142
  • [42] Fast Bayesian modal identification of structures using known single-input forced vibration data
    Au, Siu-Kui
    Ni, Yan-Chun
    STRUCTURAL CONTROL & HEALTH MONITORING, 2014, 21 (03) : 381 - 402
  • [43] A NEW GIBBS SAMPLING BASED BAYESIAN MODEL UPDATING APPROACH USING MODAL DATA FROM MULTIPLE SETUPS
    Bansal, Sahil
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2015, 5 (04) : 361 - 374
  • [44] Bayesian uncertainty quantification of modal parameters and RRF identification of transmission towers with limited measured vibration data
    Su, You-Hua
    Zhu, Yan-Ming
    Zhao, Chao
    Lam, Heung-Fai
    Sun, Qing
    ENGINEERING STRUCTURES, 2024, 308
  • [45] Framework for long-term structural health monitoring by computer vision and vibration-based model updating
    Lai, Yutao
    Chen, Jianye
    Hong, Qi
    Li, Zhekai
    Liu, Haitian
    Lu, Benhao
    Ma, Ruihao
    Yu, Chenxiao
    Sun, Rongjia
    Demartino, Cristoforo
    Narazaki, Yasutaka
    CASE STUDIES IN CONSTRUCTION MATERIALS, 2022, 16
  • [46] Vibration-based Bayesian model updating of an actual steel truss bridge subjected to incremental damage
    Zhou, Xin
    Kim, Chul-Woo
    Zhang, Feng-Liang
    Chang, Kai-Chun
    ENGINEERING STRUCTURES, 2022, 260
  • [47] Ambient modal identification of a primary-secondary structure by Fast Bayesian FFT method
    Au, Siu-Kui
    Zhang, Feng-Liang
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2012, 28 : 280 - 296
  • [48] Ambient vibration testing and updating of the finite element model of a simply supported beam bridge
    Araujo I.G.
    Maldonado E.
    Cho G.C.
    Frontiers of Architecture and Civil Engineering in China, 2011, 5 (3): : 344 - 354
  • [49] Probabilistic Structural Model Updating with Modal Flexibility Using a Modified Firefly Algorithm
    Feng, Zhouquan
    Wang, Wenzan
    Zhang, Jiren
    MATERIALS, 2022, 15 (23)
  • [50] A novel Metropolis-within-Gibbs sampler for Bayesian model updating using modal data based on dynamic reduction
    Das, Ayan
    Kiran, Raj Purohit
    Bansal, Sahil
    STRUCTURAL ENGINEERING AND MECHANICS, 2023, 87 (01) : 1 - 18