#5613. Performance study of iterative Bayesian filtering to develop an efficient calibration framework for DEM

August 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Geotechnical Engineering and Engineering Geology;
Computer Science Applications;
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Abstract:
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.
Keywords:
Bayesian calibration; Convergence; Discrete Element Method (DEM); Machine learning; Multi-objective optimisation; Triaxial compression

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