(中南大学 资源与安全工程学院，长沙 410083)
摘 要: 采用熵权法和云模型判定岩爆等级。选用岩石的单轴抗压强度σ c、单轴抗拉强度σt、切向应力σθ、岩石的压拉比σc/σt、岩石的应力系数σθ/σc和岩石的弹性变形指数Wet作为岩爆等级判定的因素建立岩爆评价指标体系。以收集到209组工程中的实际岩爆情况及数据作为样本进行分析计算，建立岩爆等级判定的熵权-云模型。运用该分析模型分析岩爆评价指标体系中评价指标的敏感性，并对收集到的工程实例岩爆情况进行判定，将结果 与Bayes、KNN和随机森林方法的判定结果进行比较。研究表明:评价指标体系中指标敏感性由大到小的顺序 为:sq /sc、sq、Wet、sc/st、st、sc；熵权-云模型的判别准确率比Bayes、K最邻近结点算法(KNN)和随机森林(RF)方法高。
关键字: 岩爆；预测；云模型；熵权；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
