(中南大學 資源與安全工程學院，長沙 410083)
摘 要: 基于254個巖爆破壞事件數(shù)據(jù)庫，采用隨機梯度提升方法(SGB)對巖爆破壞進行分類檢驗評估。SGB方法中選取 5 個可能性相關指標進行評價，包括應力條件因素、地下支護能力、地質(zhì)構造以及巖爆發(fā)生場地質(zhì)點峰值振動速度等指標。模型在評價過程中選取80%的原始數(shù)據(jù)進行建模并使用10倍交叉驗證方法評估模型的性能，然后進行外部測試，用剩余20%的數(shù)據(jù)檢驗SGB模型的預測準確性。對于多類問題模型準確性分析采用分類準確率和科恩Kappa系數(shù)兩種準確性方法。對巖爆破壞的數(shù)據(jù)準確性分析和Kappa系數(shù)的分析表明SGB模型分析法對于巖爆破壞預測是可靠的。
關鍵字: 有巖爆傾向礦山；巖爆破壞；隨機梯度提升方法
（School of Resources and Safety Engineering, Central South University, Changsha 410083, China）
Abstract:The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting (SGB) methods. Five potentially relevant indicators including the stress condition factor, the ground support system capacity, the excavation span, the geological structure and the peak particle velocity of rockburst sites were analyzed. The performance of the model was evaluated using a 10 folds cross-validation (CV) procedure with 80% of original data during modeling, and an external testing set (20%) was employed to validate the prediction performance of the SGB model. Two accuracy measures for multi-class problems were employed: classification accuracy rate and Cohen’s Kappa. The accuracy analysis together with Kappa for the rockburst damage dataset reveals that the SGB model for the prediction of rockburst damage is acceptable.
Key words: burst-prone mine; rockburst damage; stochastic gradient boosting method
