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有色金属(矿山部分):2019,71(5):93-101
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基于RS-改进云模型的岩爆倾向性预测
(1.(1. 湖南涟邵建工(集团)有限责任公司,长沙 410011;2. 中南大学 资源与安全工程学院,长沙 410083))
Application of RS and improved cloud model on predicting the rockburst propensity
(1.(1. Hunan Lianshao Construction Engineering (Group) Co., Ltd., Changsha 410011, China;2.School of Resources and Safety Engineering, Central University, Changsha 410083, China))
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中文摘要: 针对岩爆倾向性预测的模糊性和不确定性,提出了基于粗糙集理论和改进的云模型相结合的预测模型。在原有云模型的基础上,提出模糊区间的概念,进而对云模型的定义加以完善,对于云模型特征参数的求取也进行了改善。结合粗糙集理论的基本知识,把求解权重问题转变为求取属性重要度问题,进而求得综合权重,根据最大隶属度原则确定岩爆等级。选取岩石单轴抗压强度σc、岩石抗拉强度σt、切向应力σθ、弹性变形能Wet为评价因子,以国内外40组数据为学习样本,通过冬瓜山铜矿岩爆实测数据为待测样本验证其准确性。经计算,通过该模型确定的岩爆等级与实际岩爆等级基本相符,说明该模型具有较好的预测性和科学性,也为岩爆倾向性预测问题提供了新的思路。
Abstract:For the fuzziness and uncertainty of rock burst tendency prediction, this paper proposed a prediction model based on the combination of rough set theory and improved cloud model. Based on the original cloud model, the concept of fuzzy interval was proposed in order to improve the definition of cloud mode, and the evaluation of the cloud model's characteristic parameters were also improved. Combining the basic knowledge of rough set theory, the problem of solving weights was turned into the problem of attribute importance in order to obtain weight, and the rock burst level was determined according to the principle of maximum membership degree. Rock uniaxial compressive strength σc, rock tensile strength σt, tangential stress σθ, and elastic deformation energy Wet were selected as the evaluation factors, and40 sets of data at home and abroad were chosen as learning samples to verify the accuracy of the sample to be tested by the measured data of rock burst in Dongguashan Copper Mine. After calculation, the rock burst grade determined by the model was roughly consistent with the actual rock burst grade, indicating that the model had good predictability and scientific, and also providing a new idea for the rock burst tendency prediction problem.
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引用文本:
黄明健1,2,王加闯2,过 江2.基于RS-改进云模型的岩爆倾向性预测[J].有色金属(矿山部分),2019,71(5):93-101.
HUANG Mingjian1,2,WANG Jiachuang2,GUO Jiang2.Application of RS and improved cloud model on predicting the rockburst propensity[J].NONFERROUS METALS(Mining Section),2019,71(5):93-101.

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