Bayesian model selection for sand with generalization ability evaluation

Yin Fu Jin, Zhen Yu Yin, Wan Huan Zhou, Jian Fu Shao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2305-2327
Number of pages23
JournalInternational Journal for Numerical and Analytical Methods in Geomechanics
Issue number14
Publication statusPublished - 10 Oct 2019


  • Bayesian theory
  • constitutive relation
  • critical state
  • generalization ability
  • sand
  • transitional Markov chain Monte Carlo

ASJC Scopus subject areas

  • Computational Mechanics
  • Materials Science(all)
  • Geotechnical Engineering and Engineering Geology
  • Mechanics of Materials


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