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<p>In opencast coal mining, internal dump slope instability is one of the most catastrophic hazards and involves sudden and violent failure. The main reason for slope failure is unplanned dumping. This study conducted a parametric study for the internal stable dump design under geo-mining conditions. Using numerical modelling, the effect of ground floor inclination on dump slope stability in the Raniganj Coalfield mine area was investigated. The finite difference method (FDM) was used for the dump parametric study and slope factor of safety (FOS) calculation. Static, seismic, water-table, and spatial variation conditions were included in this study for the dump failure probabilistic analysis. Furthermore, artificial neural network (ANN), multiple linear regression (MLR), and long and short-term memory (LSTM) machine-learning models were developed to predict dump slope stability. The LSTM results indicate that it is a more efficient model for predicting dump stability compared to ANN and MLR. For the dump slope stability validation, drone close-range photogrammetry and realistic 3D modelling were included in this study. Additionally, optimum design parameters were implemented at the dump site. The results of this study will be helpful to the Raniganj Coalfield opencast mines for a stable dump design under geo-mining conditions.</p>

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Mine Internal Dump Optimum Geometric Design Parameters Identification for Mine Ground Floor Inclination Conditions Using Numerical Modelling and Machine Learning

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Kapoor Chand,
  • Radhakanta Koner

摘要

In opencast coal mining, internal dump slope instability is one of the most catastrophic hazards and involves sudden and violent failure. The main reason for slope failure is unplanned dumping. This study conducted a parametric study for the internal stable dump design under geo-mining conditions. Using numerical modelling, the effect of ground floor inclination on dump slope stability in the Raniganj Coalfield mine area was investigated. The finite difference method (FDM) was used for the dump parametric study and slope factor of safety (FOS) calculation. Static, seismic, water-table, and spatial variation conditions were included in this study for the dump failure probabilistic analysis. Furthermore, artificial neural network (ANN), multiple linear regression (MLR), and long and short-term memory (LSTM) machine-learning models were developed to predict dump slope stability. The LSTM results indicate that it is a more efficient model for predicting dump stability compared to ANN and MLR. For the dump slope stability validation, drone close-range photogrammetry and realistic 3D modelling were included in this study. Additionally, optimum design parameters were implemented at the dump site. The results of this study will be helpful to the Raniganj Coalfield opencast mines for a stable dump design under geo-mining conditions.