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A time function-based prediction model of mining subsidence: application to the Barapukuria coal mine, Bangla.
Zhang, Xingsheng; Yan, Shaobin; Tan, Haicheng; Dong, Jinyu.
Afiliação
  • Zhang X; School of Earth Sciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China. xingsheng.zhang@ncwu.edu.cn.
  • Yan S; School of Earth Sciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
  • Tan H; School of Earth Sciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
  • Dong J; School of Earth Sciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China. dongjy0552@126.com.
Sci Rep ; 12(1): 18433, 2022 Nov 01.
Article em En | MEDLINE | ID: mdl-36319670
Coal mining may lead to ground subsidence in a long term and is widely distributed, which can cause environmental damage and other disasters. Predicting the dynamic process of ground subsidence in real time is very important for offering theoretical or technical guidance to deal with the consequences of mining. In this study, we developed a prediction method for dynamic ground subsidence using a time function model that considers two stages of surface subsidence and reflects the law of surface subsidence in goaf. We applied the model to the Barapukuria mine, and our simulation shows that the prediction results are in good agreement with the monitoring data. Our results suggest that the dynamic development of the ground subsidence basin may be an effective measure to assess the loss of ground and provide early warning of oncoming hazards.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China
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