Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer
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 m...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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Elsevier
2018-07-01
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Series: | International Journal of Mining Science and Technology |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2095268618300946 |
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author | Defeng Hou Dehai Li Guosheng Xu Yanbin Zhang |
author_facet | Defeng Hou Dehai Li Guosheng Xu Yanbin Zhang |
author_sort | Defeng Hou |
collection | DOAJ |
description | 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. Keywords: Thick loose layer, Dynamic groundsubsidence, Kelvin visco-elastic rheological model, Random medium, Single probability integral model, Superposition model |
first_indexed | 2024-12-10T22:25:46Z |
format | Article |
id | doaj.art-ec7546f6aa8c45e69b58b70aeb702ff6 |
institution | Directory Open Access Journal |
issn | 2095-2686 |
language | English |
last_indexed | 2024-12-10T22:25:46Z |
publishDate | 2018-07-01 |
publisher | Elsevier |
record_format | Article |
series | International Journal of Mining Science and Technology |
spelling | doaj.art-ec7546f6aa8c45e69b58b70aeb702ff62022-12-22T01:31:12ZengElsevierInternational Journal of Mining Science and Technology2095-26862018-07-01284663668Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layerDefeng Hou0Dehai Li1Guosheng Xu2Yanbin Zhang3College of Resources & Safety Engineering, China University of Mining & Technology, Beijing 100083, China; Corresponding author.Institute of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaInstitute of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaInstitute of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaThe 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. Keywords: Thick loose layer, Dynamic groundsubsidence, Kelvin visco-elastic rheological model, Random medium, Single probability integral model, Superposition modelhttp://www.sciencedirect.com/science/article/pii/S2095268618300946 |
spellingShingle | Defeng Hou Dehai Li Guosheng Xu Yanbin Zhang Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer International Journal of Mining Science and Technology |
title | Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
title_full | Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
title_fullStr | Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
title_full_unstemmed | Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
title_short | Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
title_sort | superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer |
url | http://www.sciencedirect.com/science/article/pii/S2095268618300946 |
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