Microparameters Calibration for Discrete Element Method Based on Gaussian Processes Response Surface Methodology

Microparameter calibration is an important problem that must be solved in the discrete element method. The Gaussian process (GP) response surface methodology was proposed to calibrate the microparameters based on the Bayesian principle in machine-learning methods, which addresses the problems of uncertainty, blindness, and repeatability in microparameter calibration methods. Using the particle flow code (PFC) as an example, the effects of the microparameters on the macroparameters were evaluated using the control-variable method, and the range of the microparameters was determined based on the macroparameters. The uniform design (UD) method and numerical calculation were used to obtain training samples, and a GP response surface methodology suitable for multifactor, multilevel, and nonlinear processes was used to establish the response surface relationships for macro-micro parameters of rock-like materials in discrete element method. According to the macroparameters obtained from the uniaxial experiments conducted on rock specimens, the microparameters were calibrated using the GP response surfaces. Numerical calculations of uniaxial compression and Brazilian splitting were performed using microparameters, and the results were compared with laboratory experiments for verification. The results showed that the relative errors of the GP response surface and laboratory test values were 5.3% for the modulus of elasticity, -7.8% for compressive strength, and -2.6% for tensile strength. The nonlinear GP response surface considered the characteristics of multiple interacting factors, and the established nonlinear response surface relationship between the microparameters and macroparameters can be used for the calibration of microparameters. The accuracy of the microparameters was verified according to the stress-strain curve and failure morphology of the rock specimens. The method of using the GP response surface to establish the macro-micro parameter relationship in the discrete element method can also be extended to other numerical simulation methods and can provide a basis for accurately analysing the microdamage mechanism of rock materials under complex loading conditions.

Automated summary

This study proposes a microparameter calibration method based on Gaussian process (GP) response surface methodology, which addresses the issues of uncertainty, blindness, and repeatability in microparameter calibration. The study establishes a nonlinear response surface relationship between micro and macro parameters of rock materials. Experimental verification shows the accuracy of this method.