Mining Subsidence Prediction Model and Parameters Inversion in Mountainous Areas

Bang Zhou, Yueguan Yan, Huayang Dai, Jianrong Kang, Xinyu Xie and Zhimiao Pei
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Bang Zhou: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Yueguan Yan: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Huayang Dai: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Jianrong Kang: School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
Xinyu Xie: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Zhimiao Pei: College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China

Sustainability, 2022, vol. 14, issue 15, 1-23

Abstract: Coal mining in mountainous areas is general in China, especially in Shanxi Province. Under the influence of topography in mountainous areas, surface collapses and landslides caused by underground mining happen at a certain frequency and threaten human lives and assets. Accurate prediction of the movement and deformation of mining subsidence in mountainous areas facilitates the prevention and control of geological disasters. The probability integral method is an official prediction method for mining subsidence prediction in China, while it is lacking in the prediction accuracy in mountainous areas due to the inherent topography. Therefore, a practical prediction model based on slopes slip combined parameters optimization was proposed in this study. The slip subsidence and slip horizontal movement were deduced based on the probability integral method considering the topography (slope angle α < 30°) and geological conditions (loess covered) to build the prediction model. The dynamic step fruit fly optimization algorithm (DSFOA) was applied for parameters inversion about the probability integral method in the proposed prediction model, while the other parameters in the proposed model were determined by mechanics analysis based on the nature of losses. The determination of parameters is more efficient, objective and reasonable, so that the prediction accuracy can be improved. The measured data of the working panel 22,101 located in Taiyuan, Shanxi Province was verified by this practical model, and the result shows that the mean square error of subsidence and the horizontal movement was decreased to 71 mm and 276 mm, respectively, hence, the applicability of the proposed mining subsidence prediction model in mountainous areas is verified. This work will contribute to a comprehensive understanding on the law of surface movement and provide theoretical guidance for surface damage prevention and control in mountainous mining areas.

Keywords: probability integral method; slopes slip; mountainous areas; dynamic step fruit fly optimization algorithm; parameters inversion (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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