Journal
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
Volume 43, Issue 14, Pages 2305-2327Publisher
WILEY
DOI: 10.1002/nag.2979
Keywords
Bayesian theory; constitutive relation; critical state; generalization ability; sand; transitional Markov chain Monte Carlo
Funding
- Macau Science and Technology Development Fund [FDCT/125/2014/A3]
- National Natural Science Foundation of China [51579179]
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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.
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