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Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method
Yin-Fu Jin1; Zhen-Yu Yin1; Wan-Huan Zhou2; Suksun Horpibulsuk3
2019-07-01
Source PublicationActa Geotechnica
ISSN1861-1125
Volume14Issue:6Pages:1925-1947
Abstract

Parameter identification using Bayesian approach with Markov Chain Monte Carlo (MCMC) has been verified only for certain conventional simple constitutive models up to now. This paper presents an enhanced version of the differential evolution transitional MCMC (DE-TMCMC) method and a competitive Bayesian parameter identification approach for applying to advanced soil models. To realize the intended computational savings, a parallel computing implementation of DE-TMCMC is achieved using the single program/multiple data technique in MATLAB. To verify its robustness and effectiveness, synthetic numerical tests with/without noise and real laboratory tests are used for identifying the parameters of a critical state-based sand model based on multiple independent calculations. The original TMCMC is also used for comparison to highlight that DE-TMCMC is highly robust and effective in identifying the parameters of advanced sand models. Finally, the proposed parameter identification using DE-TMCMC is applied to identify parameters of an elasto-viscoplastic model from two in situ pressuremeter tests. All results demonstrate the excellent ability of the enhanced Bayesian parameter identification approach on identifying parameters of advanced soil models from both laboratory and in situ tests.

KeywordBayesian Parameter Identification Constitutive Model Clay Pressuremeter Sand Transitional Markov Chain Monte Carlo
DOI10.1007/s11440-019-00847-1
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Geological
WOS IDWOS:000497282200018
PublisherSPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
Scopus ID2-s2.0-85068793842
Fulltext Access
FWCI4.437885
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorZhen-Yu Yin
Affiliation1.Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom, Kowloon,Hong Kong
2.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,Macau,China
3.School of Civil Engineering,Suranaree University of Technology,Muang District,Thailand
Recommended Citation
GB/T 7714
Yin-Fu Jin,Zhen-Yu Yin,Wan-Huan Zhou,et al. Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method[J]. Acta Geotechnica,2019,14(6):1925-1947.
APA Yin-Fu Jin,Zhen-Yu Yin,Wan-Huan Zhou,&Suksun Horpibulsuk.(2019).Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method.Acta Geotechnica,14(6),1925-1947.
MLA Yin-Fu Jin,et al."Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method".Acta Geotechnica 14.6(2019):1925-1947.
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