Microparameter calibration for matching macroscopic responses of particle flow code 3D (PFC3D) models is generally conducted through trial-and-error which is inefficient and time-consuming. Several automatic calibration methods have been proposed but they are still limitations in the number of calibratable microparameters, range of macroscopic responses and degree of freedom in user-defined constraints. To overcome such limitations, a novel calibration method is proposed utilizing the constrained optimization of an artificial neural network (ANN). The ANN is trained with 600 PFC3D simulations to predict the unconfined compressive strength (UCS), Young’s modulus (E) and Poisson’s ratio (v) of a PFC3D model for a given set of 15 microparameter values. Unlike other ANN-based DEM calibration methods, the proposed method calibrates microparameters by optimizing the ANN inputs rather than obtaining the calibrated values as the ANN outputs. The integration of a PFC3D-mimicking ANN with constrained optimization enables microparameter calibration for a wider range of microparameters, macroscopic responses and a higher degree of freedom in user-defined constraints.
For fast DEM microparameter calibration, several automatic calibration methods have been proposed for commercial DEM codes such as particle flow code 2D/3D (PFC2D/3D). Yoon [1] proposed a calibration scheme for PFC2D based on a design of experiment (DOE) approach to derive relations between 7 PFC2D microparameters and its resulting PFC2D UCS, E and v. Obtained linear and non-linear relations were used to calibrate the microparameters by optimizing the relations with desired UCS, E and v. Similarly, Simone et al. [2] proposed calibration by applying optimization algorithms to functions explaining the relations between 5 DEM microparameters and the macroscopic responses. In their study, the functions were constructed using genetic programing, a machine learning method based on natural selection. On the other hand, instead of constructing functions between macroscopic responses and microparameters, Wang and Cao [3] employed the improved simulated annealing algorithm to automatically run PFC2D/3D simulations for calibration, automating the trial-and-error procedure.