A DEM Parameter Calibration Method Based on BP Neural Network and Genetic Algorithm

• Published in: International journal for numerical and analytical methods in geomechanics Vol. 49; no. 16; pp. 3897 - 3916
• Main Authors: Ni, Yaodong, Leng, Xianlun, Wang, Ruirui, Xia, Fengmin, Wang, Feng, Wang, Chengtang
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.11.2025
• Subjects: Algorithms; Back propagation networks; BP neural network; Calibration; Computer simulation; Discrete element method; discrete element method (DEM); genetic algorithm (GA); Genetic algorithms; Mathematical models; Neural networks; parameter calibration; Parameters; particle flow code (PFC); Simulation models
• ISSN: 0363-9061; 1096-9853
• DOI: 10.1002/nag.70043

ABSTRACT The discrete element method (DEM) represents a crucial numerical simulation approach for investigating the internal damage mechanisms of rocks. However, in order to construct an accurate simulation model, it is essential to set the correct microscopic parameters. Consequently, parameter calibration has emerged as a key area of focus within this field. The existing parameter calibration methods have yielded satisfactory results; however, there is still scope for further improvement and advancement. In this study, a novel intelligent parameter calibration method has been proposed, combining the benefits of the BP neural network and genetic algorithm (GA). The method constructs a parameter relationship model with micro‐parameters as inputs and macro‐parameters as outputs. Then GA is employed to invert the relationship model to calculate the parameter calibration. The results demonstrate that the method is capable of calculating a set of high‐precision micro‐parameter solutions in a mere 2 min, with the majority of its errors being within 5%.