Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer

被引:22
|
作者
Hou, Defeng [1 ]
Li, Dehai [2 ]
Xu, Guosheng [2 ]
Zhang, Yanbin [2 ]
机构
[1] China Univ Min & Technol, Coll Resources & Safety Engn, Beijing 100083, Peoples R China
[2] Henan Polytech Univ, Inst Energy Sci & Engn, Jiaozuo 454000, Peoples R China
基金
中国国家自然科学基金;
关键词
Thick loose layer; Dynamic groundsubsidence; Kelvin visco-elastic rheological model; Random medium; Single probability integral model; Superposition model; SURFACE SUBSIDENCE; PREDICTION;
D O I
10.1016/j.ijmst.2018.02.003
中图分类号
TD [矿业工程];
学科分类号
0819 ;
摘要
The dynamic ground subsidence due to underground mining is a complicated time-dependent and rate-dependent process. Based on the theory of rock rheology and probability integral method, this study developed the superposition model for the prediction and analysis of the ground dynamic subsidence in mining area of thick loose layer. The model consists of two parts (the prediction of overlying bedrock and the prediction of thick loose layer). The overlying bedrock is regarded as visco-elastic beam, of which the dynamic subsidence is predicted by the Kelvin visco-elastic rheological model. The thick loose layer is regarded as random medium and the ground dynamic subsidence is predicted by the probability integral model. At last, the two prediction models are vertically stacked in the same coordinate system, and the bedrock dynamic subsidence is regarded as a variable mining thickness input into the prediction model of ground dynamic subsidence. The prediction results obtained were compared with actual movement and deformation data from Zhao I and Zhao II mine, central China. The agreement of the prediction results with the field measurements show that the superposition model (SM) is more satisfactory and the formulae obtained are more effective than the classical single probability integral model (SPIM), and thus can be effectively used for predicting the ground dynamic subsidence in mining area of thick loose layer. (C) 2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:663 / 668
页数:6
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