In this paper, we employ a Bayesian approach to estimate the parameters of a high cycle accumulation model for sands using experimental data. Global sensitivity analysis and Markov-Chain Monte Carlo simulation are conducted for each of the twenty-four available experimental drained triaxial test results, considering the effect of estimating soil parameters at each strain-cycle under several loading conditions. Probability distributions inferred from each data source are then combined to obtain a single distribution for model parameters. Model calibration is then validated against new observations. The accumulated strain model is calibrated through explicit computation of strain at each cycle and the strain dependence of model parameters is included through the cyclic variation of the model constants.
|Journal||Computers and Geotechnics|
|State||Published - Jul 2022|
Bibliographical noteFunding Information:
R. Astroza and C. Pastén acknowledge the support from the Chilean National Research and Development Agency (ANID), FONDECYT projects No. 1200277 and 1190995, respectively. M. Birrell acknowledges the support of ANID through Beca Doctorado Nacional No. 21210182.
© 2022 Elsevier Ltd
- Bayesian estimation
- High cycle accumulation model
- Sensitivity analysis