Mechanism of mining subsidence in coal mining area with thick alluvium soil in China

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• 作者：Da-Wei Zhou (1)
  Kan Wu (1)
  Gong-Lin Cheng (2)
  Liang Li (1)
  
  1. School of Environment Science and Spatial Informatics ; China University of Mining and Technology ; Xuzhou ; Jiangsu ; 221116 ; People鈥檚 Republic of China
  2. National Engineering Laboratory of Coal Mine Ecological Environment Protection ; Huainan Mining Industry (Group) Corporation Limited ; Huainan ; Anhui ; 232001 ; People鈥檚 Republic of China
  
• 关键词：Thick alluvium soil ; Mining subsidence ; Similar material simulation ; Additional subsidence ; Synergetic effect of rock and soil
• 刊名：Arabian Journal of Geosciences
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：8
• 期：4
• 页码：1855-1867
• 全文大小：2,020 KB
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• 刊物类别：Earth and Environmental Science
• 出版者：Springer Berlin Heidelberg
• ISSN：1866-7538

文摘

Based on a wealth of measured data and the special occurrence condition of coal seam in the Huainan coal mining area of China, this paper proposes a new idea, which is to first separate the overlying bedrock and quaternary alluvium upon coal seam, then study regularities of mining subsidence, respectively, and finally, make a comprehensive research. A method of combining the physical simulation, theoretical analysis, and experimental data is adopted; the internal mechanism behavior and the characteristics of the surface subsidence in coal mining areas with thick alluvium are investigated. The results show that the subsidence in thick alluvium coal mining areas consists of four parts: the subsidence of alluvium soil following the bedrock subsidence caused by coal mining; the synergy subsidence between alluvium soil and bedrock; the subsidence due to groundwater loss and consolidation of the alluvium soil in coal mining area; and the compacting subsidence of shallow soil under the disturbance of coal mining. This theory can be used to completely explain the particularity of surface subsidence in coal mining areas with thick alluvium and provide the basis for establishing the prediction model.