UM
Vectorization and distributed parallelization of Bayesian model updating based on a multivariate complex-valued probabilistic model of frequency response functions
Yan,Wang Ji1; Cao,Shi Ze2; Ren,Wei Xin3; Yuen,Ka Veng1; Li,Dan2; Katafygiotis,Lambros4
2021-07-01
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume156
AbstractThis 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 Distributed parallel computing Frequency response function Model updating Structural health monitoring Vectorization computation
DOI10.1016/j.ymssp.2021.107615
URLView the original
Language英语
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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
Recommended Citation
GB/T 7714
Yan,Wang Ji,Cao,Shi Ze,Ren,Wei Xin,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 Yan,Wang Ji,Cao,Shi Ze,Ren,Wei Xin,Yuen,Ka Veng,Li,Dan,&Katafygiotis,Lambros.(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 Yan,Wang Ji,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).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yan,Wang Ji]'s Articles
[Cao,Shi Ze]'s Articles
[Ren,Wei Xin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yan,Wang Ji]'s Articles
[Cao,Shi Ze]'s Articles
[Ren,Wei Xin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yan,Wang Ji]'s Articles
[Cao,Shi Ze]'s Articles
[Ren,Wei Xin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.