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Application of a superposition model to evaluate surface asymmetric settlement in a mining area with thick bedrock and thin loose layer.

Authors :
Ma, Junbiao
Yin, Dawei
Jiang, Ning
Wang, Sheng
Yao, Dehao
Source :
Journal of Cleaner Production. Sep2021, Vol. 314, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

Surface subsidence caused by underground mining is a complicated spatiotemporal process. This study establishes a novel mining subsidence superposition model using variable mining thickness of coal seam by combining two models based on rock rheology theory and the Weibull subsidence method. This model better characterises the surface subsidence mechanism under geological conditions of thick bedrock and thin loose layer, and the prediction results are close to actual measurements. The bedrock and loose layer are considered viscoelastic beam and homogeneous body, respectively. The bedrock and loose layer are analysed using the theories of mechanical movement and mathematical models, respectively. Based on actual geological conditions of a mine in East China, the mechanical parameters were substituted into the Weibull composite subsidence prediction model (WCSPM) and the probability integral composite subsidence prediction model (PICSPM), and the predictions of surface subsidence movement parameters were obtained. The prediction results of the WCSPM are closer to the measured surface subsidence values, thereby verifying the applicability of the theoretical model in engineering practice. This model enables the construction of structures in mining-induced subsidence areas with confidence. • Subsidence deformation of thick bedrock studied using rheology theory. • Weibull distribution equation was used to study deformation of thin loose layer. • The subsidence deformation curves compounded to form novel superposition model. • Model accuracy verified by comparing predicted and actual mining subsidence data. • Dual medium-based WCSPM more suitable for obtaining actual ground subsidence curve. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
314
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
151719066
Full Text :
https://doi.org/10.1016/j.jclepro.2021.128075