煤矿开采中突透水检测一直是存在的技术难题,存在监测手段及监测方法落后的问题,笔者提出了沿工作面优化布置传感器的新方法,综合考虑各种因素如巷道、含水层水位,顶、底板压力以及挖掘面前方水量等,同时参考现有水文地质资料及监测所得数据,建立基于BP神经网络和D-S证据理论两级融合的煤矿工作面突水评价模型.评价模型以山西某矿3#煤层作为实例进行了分析,分析结果与实际结果基本相同,又提出针对不同环境条件矿井的多参数统计、存储及评价方法.%In view of the problem of lacking means of real-time monitoring and detection methods during excavation in coal mine, we proposed scheme of arrangement of sensors in working surface area. By considering such factors as roadway water level, aquifer water level, top, bottom plate pressure and water tunneling front side factors,and combining hydro geological information and monitoring data, we established the evaluation model of water bursting based on BP neural network and D-S evidence theory. The 3 # coal seam in one coal mine in Shanxi was analyzed, the forecast results and the actual results agreed well. A multi-parameter statistics, storage and e-valuation method was also proposed for the coal mines with different conditions.
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