Developing a Model for the Identification of Onset of Failure of Slopes in Surface Mines

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Authors

  • Mohd Maneeb Masood
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India ,IN
  • Tarun Verma
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005 ,IN
  • Gunda Yuga Raju
     Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005 ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/33714

Keywords:

Mine slope, slope monitoring; slope stability radar; onset-of-failure; early warning; prediction

Abstract

Slope failures in mines pose a serious concern, resulting in fatalities and significant damage to infrastructure and equipment. Despite their complexities, the majority of these slope behaviours are predictable. Mining firms worldwide have long worked on preventing mine slope failures because of the potential for human and material casualties. As a result, various investigations and studies have been done to understand these phenomena and their progression better. In this research, an automated technique is developed to track displacement data and identify the onset of slope failure, which is also the onset of acceleration. A five-step process is employed for a specific dataset. When the analysis produces a positive result for a particular set of slope monitoring data, the slope is accelerating. It helps us to determine the beginning of the slope’s acceleration. The generated output may be used as an early warning system that forecasts slope failures to avert potentially catastrophic slope occurrences, boosting overall mining performance and safety standards.

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Published

2023-05-24

How to Cite

Masood, M. M., Verma, T., & Gunda Yuga Raju. (2023). Developing a Model for the Identification of Onset of Failure of Slopes in Surface Mines. Journal of Mines, Metals and Fuels, 71(3), 327–332. https://doi.org/10.18311/jmmf/2023/33714

Issue

Volume 71, Issue 3, March 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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