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Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions
Wang-Ji Yan1; Shi-Ze Cao2; Wei-Xin Ren3; Ka-Veng Yuen1; Dan Li2; Lambros Katafygiotis4
2021-02-03
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume156
Abstract

This study was devoted to investigating stochastic model updating in a Bayesian inference framework based on a frequency response function (FRF) vector without any post-processing such as smoothing and windowing. The statistics of raw FRFs were inferred with a multivariate complex-valued Gaussian ratio distribution. The likelihood function was formulated by embedding the theoretical FRFs that contained the model parameters to be updated in the class of the probability model of the raw FRFs. The Transitional Markov chain Monte Carlo (TMCMC) used to sample the posterior probability density function implies considerable computational toll because of the large batch of repetitive analyses of the forward model and the increasing expense of the likelihood function calculations with large-scale loop operations. The vectorized formula was derived analytically to avoid time-consuming loop operations involved in the likelihood function evaluation. Furthermore, a distributed parallel computing scheme was developed to allow the TMCMC stochastic simulation to run across multiple CPU cores on multiple computers in a network. The case studies demonstrated that the fast-computational scheme could exploit the availability of high-performance computing facilities to drastically reduce the time-to-solution. Finally, parametric analysis was utilized to illustrate the uncertainty propagation properties of the model parameters with the variations of the noise level, sampling time, and frequency bandwidth.

KeywordBayesian Theory Frequency Response Function Model Updating Structural Health Monitoring Vectorization Computation Distributed Parallel Computing
DOI10.1016/j.ymssp.2021.107615
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000631590300001
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
Scopus ID2-s2.0-85100234610
Fulltext Access
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorWang-Ji Yan; Dan Li
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,China
2.Department of Civil Engineering,Hefei University of Technology,Hefei,China
3.Department of Civil Engineering,Shenzhen University,China
4.Department of Civil and Environmental Engineering,Hongkong University of Science and Technology,Hong Kong
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang-Ji Yan,Shi-Ze Cao,Wei-Xin Ren,et al. Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions[J]. Mechanical Systems and Signal Processing,2021,156.
APA Wang-Ji Yan,Shi-Ze Cao,Wei-Xin Ren,Ka-Veng Yuen,Dan Li,&Lambros Katafygiotis.(2021).Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions.Mechanical Systems and Signal Processing,156.
MLA Wang-Ji Yan,et al."Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions".Mechanical Systems and Signal Processing 156(2021).
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