Influence of grading on the undrained behavior of granular materials

Liu, Yingjing; Li, Gang; Yin, Zhen‐Yu; Dano, Christophe; Hicher, Pierre‐Yves; Xia, Xiaohe; Wang, Jianhua

doi:10.1016/j.crme.2013.11.001

Cited by 59 publications

(25 citation statements)

References 29 publications

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“…The MAP parameters that correspond to the maximum values of their posterior PDFs and the uncertainties for all selected model classes are summarized in Tables . It can be seen that some parameters with physical meanings (eg, friction angle and critical state parameters) identified from three tests for SIMSAND, SANISAND, HYPOSAND, and MCCSAND agree well with experimental measurements by Liu et al and Li et al…”

Section: Resultssupporting

confidence: 76%

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Bayesian model selection for sand with generalization ability evaluation

Jin

Yin

Zhou

et al. 2019

Num Anal Meth Geomechanics

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Summary 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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Section: Resultssupporting

confidence: 76%

“…A series of laboratory triaxial tests performed on Hostun sand by Liu et al and Li et al were chosen for the model selection. The initial void ratio and confining pressure for each test are listed in Table .…”

Section: Selection Of Sand Modelsmentioning

confidence: 99%

Bayesian model selection for sand with generalization ability evaluation

Jin

Yin

Zhou

et al. 2019

Num Anal Meth Geomechanics

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“…In order to evaluate the performance of each combination, the optimal set of parameters was applied to simulate five drained tests and six undrained tests with different confining pressures and void ratios on the same Hostun sand, as performed by Liu et al . and Li et al . .…”

Section: Selection Of Test Type For Identification Of Parametersmentioning

confidence: 89%

“…For adding undrained tests, one to four more tests (marked as 3 + 1, 3 + 2, 3 + 3 and 3 + 4) were examined, based on the available tests carried out by Liu et al . and Li et al . .…”

Section: Estimation Of Minimum Number Of Tests For Identification Of mentioning

confidence: 89%

Selection of sand models and identification of parameters using an enhanced genetic algorithm

Jin

Yin

Shen

et al. 2015

Num Anal Meth Geomechanics

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123

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Summary Numerous constitutive models of granular soils have been developed during the last few decades. As a consequence, how to select an appropriate model with the necessary features based on conventional tests and with an easy way of identifying parameters for geotechnical applications has become a major issue. This paper aims to discuss the selection of sand models and parameters identification by using genetic algorithm. A real‐coded genetic algorithm is enhanced for the optimization with high efficiency. Models with gradually varying features (elastic‐perfectly plastic modelling, nonlinear stress–strain hardening, critical state concept and two‐surface concept) are selected from numerous sand models as examples for optimization. Conventional triaxial tests on Hostun sand are selected as the objectives in the optimization. Four key points are then discussed in turn: (i) which features are necessary to be accounted for in constitutive modelling of sand; (ii) which type of tests (drained and/or undrained) should be selected for an optimal identification of parameters; (iii) what is the minimum number of tests that should be selected for parameter identification; and (iv) what is the suitable and least strain level of objective tests to obtain reliable and reasonable parameters. Finally, a useful guide, based on all comparisons, is provided at the end of the discussion. Copyright © 2015 John Wiley & Sons, Ltd.

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“…According to published experimental results of shear tests on sand and glass beads where no crushing occurred [14,21], as well as numerical simulations of uncrushable granular materials using the discrete element method [21,30,37], the critical state friction angle (i.e., constant shear at constant volume when shear strain increases) is not affected by the GSD, provided that the shape of the grains remains unchanged. Moreover, in crushable materials, it has also been shown that the critical friction angle does not depend on the breakage ratio [10,23]-i.e., for samples tested at different confining pressures and/or different straining.…”

Section: Large Triaxial Testsmentioning

confidence: 99%