Prediction of Rock Unloading Strength Based on PSO-XGBoost Hybrid Models

被引:3
作者
Liu, Baohua [1 ]
Lin, Hang [1 ]
Chen, Yifan [1 ]
Yang, Chaoyi [1 ,2 ]
机构
[1] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China
[2] Yunan Diqing Nonferrous Met Co Ltd, Shangri La 674400, Peoples R China
基金
中国国家自然科学基金;
关键词
rock strength; unloading; decreasing confining stress and increasing axial stress; prediction modeling; machine learning; MECHANICAL CHARACTERISTICS; DEFORMATION; FAILURE; MARBLE; PATH; BEHAVIOR;
D O I
10.3390/ma17174214
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for better prediction. In order to verify the validity and accuracy of the proposed hybrid model, 134 rock sample sets containing various common rock types in rock excavation were collected from international and Chinese publications for the purpose of modeling, and the rock unloading strength prediction results were compared with those obtained by the Random Forest (RF) model, the Support Vector Machine (SVM) model, the XGBoost (XGBoost) model, and the Grid Search Method-based XGBoost (GS-XGBoost) model. Meanwhile, five statistical indicators, including the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), were calculated to check the acceptability of these models from a quantitative perspective. A review of the comparison results revealed that the proposed PSO-XGBoost hybrid model provides a better performance than the others in predicting rock unloading strength. Finally, the importance of the effect of each input feature on the generalization performance of the hybrid model was assessed. The insights garnered from this research offer a substantial reference for tunnel excavation design and other representative projects.
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页数:18
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