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Bayesian model selection for sand with generalization ability evaluation
Yin‐Fu Jin1; Zhen‐Yu Yin1; Wan‐Huan Zhou2; Jian‐Fu Shao3
2019-07-17
Source PublicationInternational Journal for Numerical and Analytical Methods in Geomechanics
ISSN0363-9061
Volume43Issue:14Pages:2305-2327
Abstract

Current studies have focused on selecting constitutive models using optimization methods or selecting simple formulas or models using Bayesian methods. In contrast, this paper deals with the challenge to propose an effective Bayesian-based selection method for advanced soil models accounting for the soil uncertainty. Four representative critical state-based advanced sand models are chosen as database of constitutive model. Triaxial tests on Hostun sand are selected as training and testing data. The Bayesian method is enhanced based on transitional Markov chain Monte Carlo method, whereby the generalization ability for each model is simultaneously evaluated, for the model selection. The most plausible/suitable model in terms of predictive ability, generalization ability, and model complexity is selected using training data. The performance of the method is then validated by testing data. Finally, a series of drained triaxial tests on Karlsruhe sand is used for further evaluating the performance.

KeywordBayesian Theory Constitutive Relation Critical State Generalization Ability Sand Transitional Markov Chain Monte Carlo
DOI10.1002/nag.2979
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Materials Science ; Mechanics
WOS SubjectEngineering, Geological ; Materials Science, Multidisciplinary ; Mechanics
CSCD IDWOS:000478152700001
PublisherWILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ
Scopus ID2-s2.0-85069870492
Fulltext Access
FWCI3.067085
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,Kowloon,Hung Hom,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.Centrale Lille,FRE 2016,LaMcube,Laboratoire de Mécanique Multiphysique Multiéchelle,University of Lille,CNRS,Lille,F-59000,France
Recommended Citation
GB/T 7714
Yin‐Fu Jin,Zhen‐Yu Yin,Wan‐Huan Zhou,et al. Bayesian model selection for sand with generalization ability evaluation[J]. International Journal for Numerical and Analytical Methods in Geomechanics,2019,43(14):2305-2327.
APA Yin‐Fu Jin,Zhen‐Yu Yin,Wan‐Huan Zhou,&Jian‐Fu Shao.(2019).Bayesian model selection for sand with generalization ability evaluation.International Journal for Numerical and Analytical Methods in Geomechanics,43(14),2305-2327.
MLA Yin‐Fu Jin,et al."Bayesian model selection for sand with generalization ability evaluation".International Journal for Numerical and Analytical Methods in Geomechanics 43.14(2019):2305-2327.
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