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<p>Strata monitoring is critical for predicting roof/pillar failure and ensuring operational safety in underground metal mines. This study analyzes vertical stress and relative convergence parameters using stress cells and Multi-Point Borehole Extensometers (MPBX) in an Indian underground copper mine employing the sublevel stoping method. Over a 365-days monitoring period, instruments were strategically deployed across four production levels (0 mRL, 60 mRL, 120 mRL, and 180 mRL) to capture critical strata behavior. The vertical stress observations revealed a maximum value of 8&#xa0;kg/cm<sup>2</sup>, a typical range for hard rock mining, with a low rate of stress change (0.004–0.015&#xa0;kg/cm<sup>2</sup>/day), indicating predictable stress redistribution. Relative convergence measurements demonstrated minor roof deformations, with displacements remaining below 3&#xa0;mm and largely confined to depths between 10–15&#xa0;m, further confirming the stability of the monitored strata. The study highlights the effectiveness of existing ground support systems in maintaining stability across the mine workings. However, it underscores the need for long-term monitoring, particularly at deeper levels, to better understand dynamic strata behavior and identify threshold values for roof fall prediction. Future research integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques can provide advanced predictive models for roof and wall collapses, enhancing safety protocols and disaster prevention strategies. This research offers valuable insights into optimizing strata monitoring practices in hard rock mining environments for improved operational safety and efficiency.</p>

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Strata monitoring for stability analysis and disaster prevention in blast hole open stoping for underground copper mine

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Gopinath Samanta,
  • Angesom Gebretsadik,
  • Fisseha Gebreegziabher,
  • Mujigela Maniteja,
  • Natsuo Okada,
  • Yoko Ohtomo

摘要

Strata monitoring is critical for predicting roof/pillar failure and ensuring operational safety in underground metal mines. This study analyzes vertical stress and relative convergence parameters using stress cells and Multi-Point Borehole Extensometers (MPBX) in an Indian underground copper mine employing the sublevel stoping method. Over a 365-days monitoring period, instruments were strategically deployed across four production levels (0 mRL, 60 mRL, 120 mRL, and 180 mRL) to capture critical strata behavior. The vertical stress observations revealed a maximum value of 8 kg/cm2, a typical range for hard rock mining, with a low rate of stress change (0.004–0.015 kg/cm2/day), indicating predictable stress redistribution. Relative convergence measurements demonstrated minor roof deformations, with displacements remaining below 3 mm and largely confined to depths between 10–15 m, further confirming the stability of the monitored strata. The study highlights the effectiveness of existing ground support systems in maintaining stability across the mine workings. However, it underscores the need for long-term monitoring, particularly at deeper levels, to better understand dynamic strata behavior and identify threshold values for roof fall prediction. Future research integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques can provide advanced predictive models for roof and wall collapses, enhancing safety protocols and disaster prevention strategies. This research offers valuable insights into optimizing strata monitoring practices in hard rock mining environments for improved operational safety and efficiency.