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A novel probability integral method segmental modified model for subsidence prediction applicable to thick loose
Tao Wei1,2, Guangli Guo3,4, Huaizhan Li1,2
1Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China.
Environmental Science and Pollution Research International
|February 24, 2023
Summary
This study introduces a modified probability integral method to accurately predict surface subsidence in thick loose layer coal mines. The new model improves prediction accuracy at the edges and within subsidence basins.
Area of Science:
- Mining Engineering
- Geotechnical Engineering
- Environmental Science
Background:
- Coal mining in thick loose layers often leads to surface subsidence exceeding theoretical predictions.
- Accurate prediction of surface impacts is crucial for managing mining operations and environmental consequences.
Purpose of the Study:
- To develop a modified probability integral method for improved surface subsidence prediction in thick loose layer coal mines.
- To enhance the accuracy of subsidence basin edge and interior predictions.
Main Methods:
- Literature review to analyze the influence of thick loose layers on probability integral method parameters.
- Development of sine and logistic modification formulas for major influence radius and subsidence coefficient.
- Construction of a novel segmented parameter modified prediction model based on modified parameters and a new subsidence basin demarcation point.
Main Results:
- The modified probability integral method model effectively reduces the convergence of the surface subsidence basin edge.
- The model demonstrates improved predicted accuracy within the subsidence basin.
- Simulated and real-world data experiments validated the model's performance.
Conclusions:
- The developed segmented parameter modified probability integral method offers a more accurate approach to predicting surface subsidence in thick loose layer mining.
- This research provides essential data for disaster warning, pollution control, ecological restoration, and urban planning in mining regions.
Keywords:
LogisticMining subsidenceParameter modificationProbability integral methodSurface deformationThick loose layerMore Related Videos
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