Improving Reliability of Prediction Results of Mine Surface Subsidence of Northern Pei County for Reusing Land Resources
Source abstract
Abstract
The accurate prediction of mine surface subsidence is directly related to the reuse area of land resources. Currently, the probability integral method is the most extensive method of surface subsidence prediction in China. However, its prediction precision largely depends on the accuracy of the selected parameters. When the mining area lacks measured data, or the geological and mining conditions change, particularly for large-scale surface subsidence prediction, the reliability of the prediction of surface subsidence is poor. Moreover, there is a lack of a systematic summary of the correct selection of prediction parameters. Based on this, the paper systematically investigated the influence of geological and mining conditions on the prediction parameters of the probability integral method. The research findings were obtained via theoretical analysis. The research outcomes can provide a scientific basis for properly selecting the prediction parameters of the probability integral method. Last, the results of this paper can be applied to predict the surface subsidence of Pei County in the north, laying the foundation for the integration of Pei County.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geomechanics and Mining Engineering · Geoscience and Mining Technology · Soil, Finite Element Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221008, China
Shui Fa Planning & Design CO., Ltd., Shui FA, Jinan 250000, China
Funding
Natural Science Foundation of Jiangsu Province · China Postdoctoral Science Foundation
References (28)
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