Combining expert opinion and instrumentation data using Bayesian networks to carry out stope collapse risk assessment

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Stope collapse is a common form of accident resulting in property loss and bodily harm in mines. There are several methods to carry out risk assessment for stope collapse incident in an underground mine. This paper presents an alternate method to determine stope collapse probability using Bayesian belief networks. The alternate methodology is designed to replace a subjective risk assessment process in a metal mine in Finland. First, the stope collapse failure mechanism specific to the underground mine was established by carrying out interviews with stake holders in the underground mine. These failure modes have been mapped using Bayesian network with the use of expert opinion. The expert opinions were obtained from the interviews and their correlation and interdependencies have been defined. Use of continuous data obtained from site instrumentation in the Bayesian network has been discussed to validate the expert opinion model and to create a near real-time risk monitoring system. Updating of failure probabilities using new evidence has been discussed using a ‘what-if’ scenario analysis and use of backward inference to carry out incident investigation in the event of a failure has been described. The paper further elaborates on how Bayesian modelling for risk assessment can be incorporated in mining to justify mitigation measures and use this as a decision-making tool. When combined with existing data collection systems in the mine, this can form the backbone for a real-time risk management system.
Original languageEnglish
Title of host publicationMining geomechanical risk 2019 : Proceedings of the First International Conference on Mining Geomechanical Risk
Place of PublicationPerth
PublisherAustralian Centre for Geomechanics
Pages85-96
Number of pages12
ISBN (Electronic)978-0-9876389-1-5
DOIs
Publication statusPublished - 9 Apr 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Mining Geomechanical Risk - Perth, Australia
Duration: 9 Apr 201911 Apr 2019
Conference number: 1

Conference

ConferenceInternational Conference on Mining Geomechanical Risk
CountryAustralia
CityPerth
Period09/04/201911/04/2019

Keywords

  • Bayesian network
  • expert opinion
  • interview
  • stope design
  • mitigation

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  • Projects

    ORMID: On-line Risk Management in Deep Mines

    Rinne, M., Uotinen, L., Mishra, R., Kantia, P., Siren, T., Caballero Hernandez, E., Janiszewski, M., Kiuru, R. & Szydlowska, M.

    01/03/201631/12/2019

    Project: Academy of Finland: Other research funding

    Cite this

    Mishra, R., Kiuru, R., Uotinen, L., Janiszewski, M., & Rinne, M. (2019). Combining expert opinion and instrumentation data using Bayesian networks to carry out stope collapse risk assessment. In Mining geomechanical risk 2019 : Proceedings of the First International Conference on Mining Geomechanical Risk (pp. 85-96). Perth: Australian Centre for Geomechanics. https://doi.org/10.36487/ACG_rep/1905_02_Mishra