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Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
Wang-Ji Yan1,2; Dimitrios Chronopoulos2; Costas Papadimitriou3; Sergio Cantero-Chinchilla2,4; Guo-Shu Zhu5
2020-09
Source PublicationJournal of Sound and Vibration
ISSN0022-460X
Volume468Issue:115083
Abstract

Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. This study presents a generic framework for probabilistic damage characterization within complex structures, based on physics-rich information on ultrasound wave interaction with existent damage. To this end, the probabilistic model of wave scattering properties estimated from measured GWs is inferred based on absolute complex-valued ratio statistics. Based on the probabilistic model, the likelihood function connecting the scattering properties predicted by a computational model containing the damage parametric description and the scattering estimates is formulated within a Bayesian system identification framework to account for measurement noise and modelling errors. The Transitional Monte Carlo Markov Chain (TMCMC) is finally employed to sample the posterior probability density function of the updated parameters. However, the solution of a Bayesian inference problem often requires repeated runs of “expensive-to-evaluate” Finite Element (FE) simulations, making the inversion procedure firmly demanding in terms of runtime and computational resources. To overcome the computational challenges of repeated likelihood evaluations, a cheap and fast Kriging surrogate model built and based on a set of training points generated with an experiment design strategy in tandem with a hybrid Wave and Finite Element (WFE) computational scheme is proposed in this study. In each “numerical experiment”, the training outputs (i.e. ultrasound scattering properties) are efficiently computed using the hybrid WFE scheme which combines conventional FE analysis with periodic structure theory. By establishing the relationship between the training outputs and damage characterization parameters statistically, the surrogate model further enhances the computational efficiency of the exhibited scheme. Two case studies including one numerical example and an experimental one are presented to verify the accuracy and efficiency of the proposed algorithm.

KeywordUltrasonic Guided Waves Damage Identification Bayesian Analysis Wave Finite Elements Uncertainty Quantification Surrogate Model
DOI10.1016/j.jsv.2019.115083
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAcoustics ; Engineering ; Mechanics
WOS SubjectAcoustics ; Engineering, Mechanical ; Mechanics
WOS IDWOS:000504016600008
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
Scopus ID2-s2.0-85075544985
Fulltext Access
FWCI3.4587407
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.Aernnova Engineering Division S.A.,Madrid,28034,Spain
5.Department of Civil and Hydraulic Engineering,Hefei University of Technology,Hefei,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang-Ji Yan,Dimitrios Chronopoulos,Costas Papadimitriou,et al. Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme[J]. Journal of Sound and Vibration,2020,468(115083).
APA Wang-Ji Yan,Dimitrios Chronopoulos,Costas Papadimitriou,Sergio Cantero-Chinchilla,&Guo-Shu Zhu.(2020).Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme.Journal of Sound and Vibration,468(115083).
MLA Wang-Ji Yan,et al."Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme".Journal of Sound and Vibration 468.115083(2020).
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