Intelligent calibration method for microscopic parameters in the discrete element method based on ensemble learning.

ABSTRACT: The Block Discrete Element Method is widely used in engineering research because it can accurately model fractured rock masses. However, the accuracy of simulations depends on selecting appropriate microscopic parameters, which cannot be directly obtained from macroscopic rock tests. Therefore, calibrating microscopic parameters is essential to ensure that the model's macroscopic physical and mechanical states align with laboratory test results. Traditional trial-and-error calibration methods are highly inefficient and computationally demanding. To address this challenge, this study randomly generated microscopic parameters for discrete block elements and established computational models for uniaxial compression, Brazilian splitting, and triaxial compression tests. Maximum-edge length of t

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC12504551 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature