Cement-based grouting has been widely used in mining engineering; its constitutive law has not been comprehensively studied. In this study, a novel constitutive law of cement-grouted coal specimens (CGCS) was developed using hybrid machine learning (ML) algorithms. Shear tests were performed on CGCS for the analysis of stress-strain curves and the preparation of the dataset. To maintain the interpretation of the trained ML models, regression tree (RT) was used as the main technique. The effect of maximum RT depth (Max_depth) on its performance was studied, and the hyperparameters of RT were tuned using the genetic algorithm (GA). The RT performance was also compared with ensemble learning techniques. The optimum correlation coefficient on the training set was determined as 0.835, 0.946, 0.981, and 0.985 for RT models with Max_depth = 3, 5, 7, and 9, respectively. The overall correlation coefficient was over 0.9 when the Max_depth ≥ 5, indicating that the constitutive law of CGCS can be well described. However, the failure type of CGCS could not be captured using the trained RT models. Random forest was found to be the optimum algorithm for the constitutive modeling of CGCS, while RT with the Max_depth = 3 performed the worst.

水泥基注浆已广泛应用于矿山工程；其构成法尚未得到全面研究。在这项研究中，使用混合机器学习 (ML) 算法开发了一种新的水泥灌浆煤试样本构律 (CGCS)。剪切试验在 CGCS 上进行，用于分析应力-应变曲线和准备数据集。为了保持对经过训练的 ML 模型的解释，回归树 (RT) 被用作主要技术。研究了最大RT深度（Max_depth）对其性能的影响，并使用遗传算法（GA）调整了RT的超参数。RT 性能也与集成学习技术进行了比较。对于 Max_depth = 3、5、7 的 RT 模型，训练集上的最佳相关系数确定为 0.835、0.946、0.981 和 0.985，和 9，分别。当Max_depth≥5时，整体相关系数大于0.9，说明CGCS的本构规律可以很好地描述。但是，使用经过训练的 RT 模型无法捕获 CGCS 的故障类型。随机森林被发现是 CGCS 本构建模的最佳算法，而 Max_depth = 3 的 RT 表现最差。