An experimental database for the development, calibration and verification of constitutive models for sand with focus to cyclic loading: part I—tests with monotonic loading and stress cycles

Wichtmann, Torsten; Triantafyllidis, Theodoros

doi:10.1007/s11440-015-0402-z

Cited by 140 publications

(90 citation statements)

References 56 publications

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“…Table summarizes the MAP parameters of all sand models for Karlsruhe sand. It can be seen that the parameters identified for SIMSNAD, SANISAND, HYPOSAND, and MCCSAND are in appropriate ranges compared with the experimental investigations ( ϕ = 33.1° and e ref = 1.067) by Wichtmann and Triantafyllidis …”

Section: Resultsmentioning

confidence: 55%

“…Satisfactory agreement is found between the numerical simulations and experiments that contractive and dilatancy behaviours, even static liquefaction, can be easily reproduced, demonstrating an excellent predictive ability for SIMSAND as well the rationality of identified parameters. 76 the test Karlsruhe sand has a mean grain size d 50 = 0.14 mm and a uniformity coefficient C u = d 60 /d 10 = 1.5. The minimum and maximum void ratios were e min = 0.677 and e max = 1.054.…”

Section: Resultsmentioning

confidence: 99%

“…A series of drained triaxial tests performed on Karlsruhe sand by Wichtmann and Triantafyllidis was selected for this case. According to Wichtmann and Triantafyllidis, the test Karlsruhe sand has a mean grain size d 50 = 0.14 mm and a uniformity coefficient C u = d 60 / d 10 = 1.5. The minimum and maximum void ratios were e min = 0.677 and e max = 1.054.…”

Section: Validation By Other Sandmentioning

confidence: 99%

See 2 more Smart Citations

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: Resultsmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: Validation By Other Sandmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian model selection for sand with generalization ability evaluation

Jin

Yin

Zhou

et al. 2019

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

“…Six sets of drained monotonic triaxial tests are used for calibrating the monotonic parameters, while memory surface-related parameters are calibrated based on the strain accumulation curve with loading cycles up to 10 4 . Table 2 lists model parameters for the Karlsruhe fine sand-experimental data published by Wichtmann and Triantafyllidis (2016). In total, 10 sets of drained and undrained monotonic triaxial tests are simulated to identify the monotonic parameters.…”

Section: Model Calibrationmentioning

confidence: 99%

“…The proposed model is validated by simulating results of experimental tests performed on a quartz sand and the Karlsruhe sand under cyclic loading conditions. The experimental data regard both drained (Wichtmann 2005) and undrained (Wichtmann and Triantafyllidis 2016) triaxial tests investigating the influence of varying the mean confining pressure, cyclic stress amplitude and void ratio.…”

Section: Introductionmentioning

confidence: 99%