In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thick...Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation.In this study,four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting(XGBoost)and random forest(RF)algorithms through simulated annealing(SA)and Bayesian optimization(BO)approaches,namely SA-XGBoost,SA-RF,BO XGBoost and BO-RF models.A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province,China,including seven input indicators:embedding depth,drift span,uniaxial compressive strength of rock,rock mass rating,unit weight of rock,lateral pressure coefficient of roadway and unit consumption of blasting explosive.The performance of the proposed models was evaluated by the coefficient of determination,root mean squared error,mean absolute error and variance accounted for.The results indicated that the SA-XGBoost model performed best.The Shapley additive explanations method revealed that the embedding depth was the most important indicator.Moreover,the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.展开更多
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
基金Project(52204117)supported by the National Natural Science Foundation of ChinaProject(2022JJ40601)supported by the Natural Science Foundation of Hunan Province,China。
文摘Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation.In this study,four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting(XGBoost)and random forest(RF)algorithms through simulated annealing(SA)and Bayesian optimization(BO)approaches,namely SA-XGBoost,SA-RF,BO XGBoost and BO-RF models.A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province,China,including seven input indicators:embedding depth,drift span,uniaxial compressive strength of rock,rock mass rating,unit weight of rock,lateral pressure coefficient of roadway and unit consumption of blasting explosive.The performance of the proposed models was evaluated by the coefficient of determination,root mean squared error,mean absolute error and variance accounted for.The results indicated that the SA-XGBoost model performed best.The Shapley additive explanations method revealed that the embedding depth was the most important indicator.Moreover,the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.
