Study on failure depth of coal seam floor in deep mining

被引:26
作者
Hu, Yanbo [1 ]
Li, Wenping [1 ]
Wang, Qiqing [1 ]
Liu, Shiliang [1 ]
Wang, Zhenkang [1 ]
机构
[1] China Univ Min & Technol, Sch Resources & Geosci, Xuzhou 221116, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Gasbag-fluid leak hunting method; Back propagation neural network (BPNN); North China-type coal mines; Coal seam mining; WATER INRUSH; MINE; DEFORMATION;
D O I
10.1007/s12665-019-8731-0
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The year-after-year deepening of North China-type coal mines has resulted in frequent water inrush disasters at coal seam floors, causing significant casualties and property loss. The key factor for assessing water inrush hazards is the depth at which the coal seam floor will fail. It is therefore of great importance to predict this failure depth so as to evaluate the potential for floor water inrush incidents and establish effective controls in a timely manner. This paper takes a deep-mining mine in North China Coalfield, as an example, a comparative study is made using four methods: theoretical calculation method, back propagation neural network (BPNN) method, field measurement method, and empirical formula method. The results show that the new theoretical calculation method and the back propagation neural network method proposed in this paper are closer to the true floor failure depth under deep seam mining conditions. Therefore, this study has played an important reference role for prevention and control of water inrush from coal seam floor in deep mining.
引用
收藏
页数:13
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