This work presents an efficient probabilistic framework for the Bayesian calibration of micro-mechanical parameters for Discrete Element Method (DEM) modelling. Firstly, the superior behaviour of the iterative Bayesian filter over the sequential Monte Carlo filter for calibrating micro-mechanical parameters is shown. The linear contact model with rolling resistance is used for simulating the triaxial responses of Toyoura sand under different confining pressures. Secondly, synthetic data from DEM simulations of triaxial compression are used to assess the reliability of iterative Bayesian filtering with respect to the user-defined parameters, such as the number of samples and predefined parameter ranges. Excellent calibration results with errors between 1 and 2% are obtained when the number of samples is chosen high enough. It is crucial that the sample size is representative for the distribution of individual parameters within the predefined parameter ranges. The wider the ranges, the more samples are required. The investigation also shows the necessity of including both stress and strain histories, at certain confidence levels, for estimation of the correct mechanical responses, especially the correct fabric responses. Finally, based on the findings of this work a fully-automated open-source calibration tool is developed and demonstrated for selected stress paths.
- Bayesian calibration
- Discrete Element Method (DEM)
- Machine learning
- Multi-objective optimisation
- Triaxial compression