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Monitoring systems for warning impending failures in slopes and open pit mines

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  • Ashkan Vaziri
  • Larry Moore
  • Hosam Ali

Abstract

Slope stability is a critical safety and production issue for mining. Major wall failure can occur seemingly without any visual warning, causing loss of lives, damage to equipment, and disruption to the mining process. Monitoring systems, ranging from simple piezometers and extensometers to highly sophisticated radars and global navigation satellite systems, are employed to predict impending instabilities and failure. Here, we provide a review of the available monitoring systems used in slope management and highlight their major advantages and shortcomings. We propose a simple method for evaluating the effectiveness and reliability of monitoring systems to warn of pending slope failures. The method is based on constructing monitoring reliability maps for the slope by evaluating two slope parameters: Expected deformation to failure and critical reading frequency, which depend on the slope characteristics (e.g., geology and design), service condition (e.g., rainfall, blast), and the economic impact of the failure. The reliability of a deformation monitoring system can be subsequently assessed by identifying three parameters of the system: Coverage area (large or discrete), Deformation monitoring precision, and Measurement frequency. The application of the method to most commonly used deformation monitoring systems is demonstrated. The advantages and implications of the proposed method are highlighted. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Ashkan Vaziri & Larry Moore & Hosam Ali, 2010. "Monitoring systems for warning impending failures in slopes and open pit mines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 55(2), pages 501-512, November.
  • Handle: RePEc:spr:nathaz:v:55:y:2010:i:2:p:501-512
    DOI: 10.1007/s11069-010-9542-5
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    Citations

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    Cited by:

    1. Renato Macciotta & Michael Hendry & C. Derek Martin, 2016. "Developing an early warning system for a very slow landslide based on displacement monitoring," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 887-907, March.
    2. Sunwen Du & Guorui Feng & Jianmin Wang & Shizhe Feng & Reza Malekian & Zhixiong Li, 2019. "A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data," Energies, MDPI, vol. 12(7), pages 1-15, April.
    3. Renato Macciotta & Michael Hendry & C. Martin, 2016. "Developing an early warning system for a very slow landslide based on displacement monitoring," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 887-907, March.
    4. C. Loupasakis & V. Angelitsa & D. Rozos & N. Spanou, 2014. "Mining geohazards—land subsidence caused by the dewatering of opencast coal mines: The case study of the Amyntaio coal mine, Florina, Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(1), pages 675-691, January.

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