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.
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