(中南大學 資源與安全工程學院，長沙 410083)
摘 要: 采用熵權法和云模型判定巖爆等級。選用巖石的單軸抗壓強度σ c、單軸抗拉強度σt、切向應力σθ、巖石的壓拉比σc/σt、巖石的應力系數(shù)σθ/σc和巖石的彈性變形指數(shù)Wet作為巖爆等級判定的因素建立巖爆評價指標體系。以收集到209組工程中的實際巖爆情況及數(shù)據(jù)作為樣本進行分析計算，建立巖爆等級判定的熵權-云模型。運用該分析模型分析巖爆評價指標體系中評價指標的敏感性，并對收集到的工程實例巖爆情況進行判定，將結果 與Bayes、KNN和隨機森林方法的判定結果進行比較。研究表明:評價指標體系中指標敏感性由大到小的順序 為:sq /sc、sq、Wet、sc/st、st、sc；熵權-云模型的判別準確率比Bayes、K最鄰近結點算法(KNN)和隨機森林(RF)方法高。
關鍵字: 巖爆；預測；云模型；熵權；敏感性
（School of Resources and Safety Engineering, Central South University, Changsha 410083, China）
Abstract:The method of cloud model with entropy weight was adopted for the prediction of rock burst classification. Some main factors of rock burst including the uniaxial compressive strength (σ c), the tensile strength (σt), the tangential stress (σθ), the rock brittleness coefficient (σc/σt), the stress coefficient (σθ /σc) and the elastic energy index (Wet) are chosen to establish evaluation index system. The entropy-cloud model and criterion are obtained through 209 sets of rock burst samples from underground rock projects. The sensitivity of indicators is analyzed and 209 sets of rock burst samples are discriminated by this model. The discriminant results of the entropy-cloud model are compared with those of Bayes, KNN and RF methods. The results show that the sensitivity order of those factors from high to low is sq /sc, sq, Wet, sc/st, st , sc, and the entropy-cloud model has higher accuracy than Bayes, K-Nearest Neighbor algorithm (KNN) and Random Forest (RF) methods.
Key words: rock burst; prediction; cloud model; entropy weight; sensitivity
