A calibration framework for DEM models based on the stress‒strain curve of uniaxial compressive tests by using the AEO algorithm and several calibration suggestions

Before the discrete element method (DEM) is implemented for numerical simulations, the microparameters of the DEM models should be calibrated. Microparameter calibration is a critically important procedure for numerical DEM simulations. The macroparameters obtained from physical tests (e.g. UCS, Young’s modulus, Poisson’s ratio) were used to calibrate the microparameters of DEM models. However, the mechanical characteristics of rock materials cannot be fully reflected by the macroparameters. Hence, in this paper, the stress‒strain relationships of uniaxial compressive tests were used for calibrating the microparameters of DEM (discrete element method) models by using the artificial ecosystem-based optimization (AEO) algorithm, combined with a Python script and a stress‒strain curve of uniaxial compressive tests from laboratory experiments. Additionally, a microparameter calibration framework was proposed. To verify the validity of the proposed method, two examples were evaluated, and the numerical simulation results indicated that the proposed method can be applied to calibrate the microparameters of DEM models. Moreover, to analyse the influence of each microparameter on the stress‒strain curve of uniaxial compressive tests, a large number of numerical simulations were conducted. Finally, based on the analysis, some microparameter calibration suggestions were provided. This study provides a new method for calibrating microparameters and provides calibration suggestions that are critically important for numerical DEM simulations.