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An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly
Wang-Ji Yan1,2; Costas Papadimitriou3; Lambros S. Katafygiotis4; Dimitrios Chronopoulos2
2020
Source PublicationMECHANICAL SYSTEMS AND SIGNAL PROCESSING
ISSN0888-3270
Volume135Pages:106376
Abstract

Assembling local mode shapes identified from multiple setups to form global mode shapes is of practical importance when the degrees of freedom (dofs) of interest are measured separately in individual setups or when one expects to exploit the computational autonomous capabilities of different setups in full-scale operational modal test. The Bayesian mode assembly methodology was able to obtain the optimal global mode shape as well as the associated uncertainties by taking the inverse of the analytically derived Hessian matrix of the negative log-likelihood function (NLLF) (Yan and Katafygiotis, 2015) [1]. In this study, we investigate how the posterior uncertainties existing in the local mode shapes obtained from different setups propagate into the global mode shapes in an explicit manner by borrowing a novel approximate analysis strategy. The explicit closed-form approximation expressions are derived to investigate the effects of various data parameters on the posterior covariance matrix of the global mode shapes. Such quantitative relationships, connecting the posterior uncertainties with global mode shapes and the data information, offer a better understanding of uncertainty propagation over the process of mode shape assembly. The posterior uncertainty of the global mode shapes is inversely proportional to ‘normalized data length’ and the ‘frequency bandwidth factor’, and propositional to ‘noise-to-environment’ ratio and damping ratio. Validation studies using field test data measured from the Metsovo bridge located in Greece provide a practical verification of the rationality of the theoretical findings of uncertainty quantification and propagation analysis in Bayesian mode shape assembly.

KeywordOperational Modal Analysis Mode Shape Assembly Bayesian Analysis Uncertainty Propagation Structural Health Monitoring
DOI10.1016/j.ymssp.2019.106376
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000505271300018
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
Scopus ID2-s2.0-85072982545
Fulltext Access
FWCI1.3137869
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorWang-Ji Yan
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,China
2.Institute for Aerospace Technology & The Composites Group,The University of Nottingham,United Kingdom
3.Department of Mechanical Engineering,University of Thessaly,Greece
4.Department of Civil and Environmental Engineering,Hong Kong 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,Costas Papadimitriou,Lambros S. Katafygiotis,et al. An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2020,135:106376.
APA Wang-Ji Yan,Costas Papadimitriou,Lambros S. Katafygiotis,&Dimitrios Chronopoulos.(2020).An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,135,106376.
MLA Wang-Ji Yan,et al."An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 135(2020):106376.
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