为解决多点接触状态和颗粒形状对堆石坝颗粒破碎影响机制不明确的问题,对480个不同粒径(d)、纵横比(AR)和加载角度(α)的石膏颗粒开展单轴压缩试验,结合数字图像相关技术(DIC)获取位移场;构建特征数据库,采用网格搜索和5折交叉验证,对比SVM、MLP、RF和XGB的破坏模式分类及强度预测性能。结果表明:①颗粒出现劈裂、压碎和剪切3类破坏,剪切破坏仅发生于3点及以上接触。②极端梯度提升(XGB)模型表现最优:测试集宏平均AUC为0.96,RMSE和MAE分别为0.9 MPa和0.8 MPa;粒径为20 mm,XGB模型预测误差仅为0.06 MPa,优于Hertz理论。研究成果揭示了形状与接触状态对破碎特性具有显著非线性影响,所建模型可为堆石料力学分析与离散元参数确定提供参考。

[Objective] To address the unclear influence mechanisms of multi-point contact states and irregular particle shapes on particle breakage modes and crushing strength in rockfill dams, and to fill the research gap in systematically integrating meso-scale mechanical features with data-driven machine learning algorithms, this study establishes a robust framework for failure mode classification and crushing strength prediction of single rockfill particles. The framework is intended to overcome the inherent limitations of traditional empirical formulas that rely on idealized spherical geometries and fixed two-point elastic contact modes. [Methods] Uniaxial compression tests were conducted on artificial gypsum particles with varying particle sizes, aspect ratios, and loading angles. Digital image correlation (DIC) technique was employed to acquire full-field surface displacement data and capture crack evolution characteristics during compression. A grid search strategy combined with stratified 5-fold cross-validation was applied to optimize hyperparameters and mitigate overfitting. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGB)—were systematically evaluated and compared for both failure mode classification and crushing strength regression tasks. [Results] Three distinct failure modes were identified: splitting, crushing, and shear. Shear failure occurred exclusively under three-point or higher contact conditions, while two-point compression produced only splitting or crushing. Crushing strength exhibited a pronounced size effect and shape dependency: strength decreased as particle size increased, aspect ratio increased, or loading angle increased. Among the four algorithms, XGB demonstrated the best overall performance. In the classification task, XGB achieved a macro-average AUC of 0.96 on the independent test set, with a macro-average precision of 0.90 and an F1-score of 0.88. In the regression task, XGB yielded the lowest RMSE and MAE of 0.9 MPa and 0.8 MPa, respectively, with R² of 0.92. Compared with four traditional strength criteria (splitting, crushing, shear failure theories, and Hertz contact theory), the XGB model demonstrated superior prediction accuracy across all particle sizes. Under the 20 mm particle size condition, the absolute deviation between the XGB-predicted value and the experimental value was only 0.06 MPa, compared with 0.09 MPa for the Hertz contact theory. [Conclusions] Particle shape and contact state exert significant nonlinear regulatory effects on the crushing characteristics of granular materials. The XGB-based machine learning model effectively overcomes the oversimplified assumptions of traditional theories regarding idealized spherical geometries and fixed contact modes. By autonomously learning from high-dimensional meso-mechanical features, the model exhibits superior generalization ability and prediction accuracy under complex conditions involving irregular particle shapes and multi-point contacts.