Particle-based discrete element modeling is commonly used in the numerical analysis of geomaterials. However, for the construction of such models, micromechanical parameters should be calibrated such that a set of microproperties must be chosen carefully to reproduce the macroscopic behavior of the geomaterial. This paper explores the use of the adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), Gaussian process regression (GPR), and multivariate adaptive regression splines (MARS) methods for predicting the uniaxial compressive strength (UCS) of the Voronoi-based universal distinct element code (UDEC) model based on microshear strength properties of contacts. The data for training and testing the ANFIS, SVM, GPR, and MARS models were obtained from 121 numerically simulated Voronoi-based UCS models. Several statistical functions (\({R}^{2}\), RMSE, MAE, and VAF) were utilized to check the performances of the predictive models. The high performance indices of the models highlight the capability of the ANFIS, SVM, GPR, and MARS (with interaction terms) models in making a rapid assessment of the calibration process.

基于粒子的离散元建模常用于岩土材料的数值分析。然而，对于此类模型的构建，应校准微观力学参数，以便必须仔细选择一组微观属性以再现地质材料的宏观行为。本文探讨了使用基于自适应网络的模糊推理系统 (ANFIS)、支持向量机 (SVM)、高斯过程回归 (GPR) 和多元自适应回归样条 (MARS) 方法来预测单轴抗压强度 (UCS)基于接触微剪切强度特性的基于 Voronoi 的通用独特元素代码 (UDEC) 模型的分析。用于训练和测试 ANFIS、SVM、GPR 和 MARS 模型的数据来自 121 个数值模拟的基于 Voronoi 的 UCS 模型。\({R}^{2}\)、RMSE、MAE 和 VAF) 用于检查预测模型的性能。模型的高性能指标突出了 ANFIS、SVM、GPR 和 MARS（具有交互项）模型在对校准过程进行快速评估方面的能力。