Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

安全智能采矿:大数据时代的若干探索与挑战

为进一步完善采矿学学科体系,针对传统采矿学理论与方法上存在的不足,立足于学科建设高度,积极推进采矿学学科理论的研究与课程内容体系的基本建设,本文开展了安全智能采矿学的建立研究。首先,根据采矿学发展进程以及国内外智能采矿的发展现状,提出了安全智能采矿学的概念,并分析了其学科内涵与学科属性。其次,从学科建设角度提出安全智能采矿学学科基础与学科任务,并构建了学科内容体系。最后,本文提出了安全智能采矿学面临的十大挑战,讨论了安全智能采矿学的未来研究任务与应用前景。安全智能采矿学的创建,是大数据时代下信息化采矿工程学科发展的必然趋势,可为安全智能采矿学学科的发展注入生机与活力。