地下突水隐患识别与风险预测的改进D-S算法  

Improved D-S Algorithm of Hazard Identification and Risk Prediction of Underground Mine Water Inrush

作　　者：阮竹恩[1,2] 李翠平[1,2] 李仲学[1,2] 

机构地区：[1]北京科技大学土木与环境工程学院,北京100083 [2]金属矿山高效开采与安全教育部重点实验室,北京100083

出　　处：《金属矿山》2014年第9期142-146,共5页Metal Mine

基　　金：国家自然科学基金项目(编号:51174260;51174032);教育部新世纪优秀人才支持计划项目(编号:NCET-10-0225)

摘　　要：针对地下矿突水因素的不确定性与时空的随机性,主要应用改进D-S算法开展地下矿突水隐患识别与风险预测,以期为地下矿突水隐患识别和水灾预测与防治等提供有效的理论依据。在考虑地质突水因素的同时,更加注重在生产管理中的人为因素。通过采用专家打分法获取各突水因素的基本概率赋值(BPA),基于改进D-S算法,构建识别框架Ω={突水、临界、不突水、不确定},建立以富水性系数、隔水层系数、水压系数、构造系数、矿压系数、勘探系数、开采系数、预防管理系数为证据体的突水融合决策模型。最后,通过国内某典型矿山进行实际应用,结果与实际情况基本一致,说明所建立的预测模型是可行的。In view of uncertain factors and time-location randomness in underground mine water inrush, the hazard identi- fication and risk prediction of underground mine water inrush was studied by improved D-S （Dempster Shafer） Algorithm, so as to provide effective theoretical support to identify hazard as well as prevent water inrushes. In consideration of natural geological factors, more attention was paid to human factors in mine production management. With expert analysis, the BPA （ Basic proba- bility assignment）were governed to the factors which affect underground mine water-inrush. Based on the improved D-S Algo- rithm, the frame of discernment Ω was proposed which includes water-inrush, critical condition, no water-inrush and uncertain information. And the water-inrush integration decision-making model was established. The evidences of the model were aquifer water-bearing ratio, water-resisting layer thickness ratio, underground water pressure ratio, structure ratio, underground pressure ratio, exploration ratio, mining ratio and preventive management ratio. Finally, in the ease of an internal typical underground mine, the results were broadly in line with the actual situation, which showed that the model was feasible and applicable.

关 键 词：矿井突水 改进D-S算法 隐患识别 突水风险预测 

分 类 号：TD745[矿业工程—矿井通风与安全]