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机构地区:[1]湖南科技大学能源与安全工程学院,湖南湘潭411201 [2]煤矿安全开采技术湖南省重点实验室,湖南湘潭411201
出 处:《煤炭学报》2012年第4期565-570,共6页Journal of China Coal Society
基 金:湖南省自然科学基金资助项目(11JJ5030);国家自然科学基金资助项目(51074071;51104063)
摘 要:针对神东矿区煤巷长度超长、巷道断面大、顶板岩层结构变化复杂及锚杆支护率高等实际情况和锚杆(索)支护的工程特性,依据围岩稳定性分类指标的科学性、实用性及可定量化的选取原则,在深入分析神东矿区锚杆支护煤巷顶板稳定性主要影响因素的基础上,研究确定了单层厚岩层离顶板表面距离L、单层厚岩层抗压强度σc、单层厚岩层厚度D、巷道跨度B、巷道埋深H、采动影响指标N六个分类指标作为神东矿区锚杆支护超长煤巷顶板稳定性分区动态分类指标,并构建了神东矿区锚杆支护超长煤巷顶板稳定性分区动态分类的BP神经网络预测模型,经检验该神经网络模型的识别准确率大于95%,利用该网络模型进行动态分类的结果与现场实际高度吻合。According to the complex changing condition of the stability of coal roadway roof,combining with Shendong super-long coal roadway,large section roadway,complex changes of roof strata structure as well as engineering properties of bolts(cables) support,based on the scientific,practicality and quantitative selection principles of the stibility classification indexes of coal roadway roof,based on the deep analysis on affect factors of stability for bolts support coal roadway roof in Shendong diggings,distance to roof surface of the single thick strata L,strength σc and thickness D,span B and depth H of the coal roadway as well as the index of mining effect N were determined as classification parameters of the dynamic stability classification of super-long bolt support coal roadway roof in Shendong Diggings,and the BP neural network prediction model of dynamic stability classification of bolt support for super-long coal roadway roof in Shendong mining area was established,the recognition accuracy of the model is more than 95% by test,and the result of dynamic stability classification is highly anastomosed with the actual site.
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