工程岩体存在原生裂纹和新生裂纹最终形成宏观裂缝，空心圆柱离散元模拟试验可以实现复杂应力路径的模拟。对于空心圆柱离散元模拟试验中存在的问题，如细观参数标定的影响因素众多和耗时过长等，提出了一种基于机器学习算法的空心圆柱砂岩离散元细观参数标定方法。通过改变离散元模型中不同输入变量得到210组模拟数据，建立基于随机森林算法和极端梯度提升（extreme gradient boosting, XGBoost）算法的细观参数标定模型，对比了模型预测精度，分析了参数敏感性，量化了输入参数对岩石整体力学特性的贡献，据此给出了微观参数修正的建议取值。结合空心圆柱室内三轴试验，标定结果表明：XGBoost算法具有计算速度优势，可快速定位离散元细观参数范围，为空心圆柱的离散元细观参数标定提供了新思路，具有工程应用价值。

Primary cracks and new cracks develop within the engineering rock mass, leading to the formation of macroscopic cracks. The hollow cylindrical discrete element simulation test enables the emulation of complex stress paths. In order to solve the problems existing in the simulation test of hollow cylindrical discrete element, such as numerous influencing factors and lengthy mesoparameter calibration, a method of mesoscale parameter calibration of hollow cylindrical sandstone discrete element based on machine learning algorithm was proposed. Through variations in input variables within the discrete element model, 210 sets of simulation data were obtained. A mesoscopic parameter calibration model based on random forest algorithm and extreme gradient boosting(XGBoost) algorithm was established, the prediction accuracy of the model was compared, the parameter sensitivity was analyzed, and the contribution of input parameters to the overall mechanical properties of the rock was quantified. Combined with the indoor triaxial test of hollow cylinder, the calibration results show that the XGBoost algorithm has the advantages of computing speed, and can quickly locate the range of discrete element mesoscopic parameters, which provides a new idea for the calibration of discrete element mesoscopic parameters of hollow cylinder, and has the value of engineering application.