Geotechnical classification and Bayesian network for real time risk assessment in mining

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School of Engineering | Doctoral thesis (article-based) | Defence date: 2019-06-24
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Date

2019

Department

Rakennustekniikan laitos
Department of Civil Engineering

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Mcode

Degree programme

Language

en

Pages

72 + app. 74

Series

Aalto University publication series DOCTORAL DISSERTATIONS, 107/2019

Abstract

Mining involves the extraction of finite resources for their use in vast number of applications. Depletion of resources over time has required mining to be carried out underground and unprecedented depths. It is therefore important to conduct geotechnical risk assessments in advance to prevent accidents and sustain economic mining operation. Extent of available geotechnical information varies for a mine as the mine progresses from feasibility to operational stage. Geotechnical risk assessment (GRA) can be incorporated into the mine planning process from as early as the pre-feasibility stage. A formal risk assessment can be planned using appropriate scope definition which can help chose from a number of risk assessment tools and parameters. The goals of the research were: design a geotechnical risk classification system, which can be used from preliminary stages of mine planning and to motivate a detailed risk assessment; develop guidelines to prepare the scope of a detailed GRA; define selection criteria to choose the appropriate hazard identification tool and risk assessment parameters; carry out risk assessment in presence and absence of historical incident data; develop a framework to carry out geotechnical risk assessment in real time; represent and communicate the final risk to the work force for mitigation planning. The proposed geotechnical risk classification system (GRC) can be used to identify, rank and communicate the hazardous sections of a mine to the work force. The guidelines developed for defining the scope of the risk assessment and the numerical ranking system for risk assessment parameter selection can be used to define the risk assessment process and choose between deterministic, probabilistic and empirical method of risk assessment. The demonstrated methodology of fault tree and event tree can be used to break down a hazard into its elemental causes and to plan against all possible outcomes following an incident. Bayesian network (BN) based risk assessment can be used to model complex causal relationship of accidents and carry out incident investigation using the same model. It was shown using parameter learning that normal distribution of mine incidents was a better fit for incident forecasting compared to Poisson distribution for the cases studied in the thesis. A new method to combine multiple probability distributions to forecast future incidents has been proposed. It was demonstrated that BN based risk assessment can incorporate expert opinion in absence of data to forecast incidents. Finally, the measured risk can be communicated and monitored graphically using the F-N diagram.

Description

Supervising professor

Rinne, Mikael, Prof., Aalto University, Department of Civil Engineering, Finland

Thesis advisor

Rinne, Mikael, Prof., Aalto University, Department of Civil Engineering, Finland

Keywords

geotechnical risk, FN diagram, risk assessment, FTA, ETA, GRC system, Bayesian network, roof collapse

Other note

Parts

  • [Publication 1]: Mishra, Ritesh; Rinne, Mikael. Geotechnical risk classification for underground mines. Archives of Mining Sciences, 2015, Volume 60, Issue 1, Pages 51 - 61.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201711217711
    DOI: 10.1515/amsc-2015-0004 View at publisher
  • [Publication 2]: Mishra, Ritesh; Rinne, Mikael. Guidelines to design the scope of a geotechnical risk assessment. Journal of Mining Science, 2014, Volume 50, Issue 4, Pages 745 - 756.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201711217579
    DOI: 10.1134/S1062739114040152 View at publisher
  • [Publication 3]: Mishra, Ritesh; Janiszewski, Mateusz; Uotinen, Lauri; Szydlowska, Martyna; Siren, Topias; Rinne, Mikael. Geotechnical Risk Management Concept for Intelligent Deep Mines. Procedia Engineering, 2017, Volume 191, pages 361-368.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201710136974
    DOI: 10.1016/j.proeng.2017.05.192 View at publisher
  • [Publication 4]: Mishra, Ritesh; Kiuru, Risto; Uotinen, Lauri; Janiszewski, Mateusz; Rinne, Mikael. Combining expert opinion and instrumentation data using Bayesian network to carry out stope collapse risk assessment. In First International Conference On Mining Geomechanical Risk, Perth, April 9 – 11, 2019. Australian Centre for Geomechanics.
  • [Publication 5]: Mishra, Ritesh; Uotinen, Lauri; Rinne, Mikael. Bayesian network approach for geotechnical risk assessment in underground mines. Manuscript submitted to Journal of Southern African Institute of Mining and Metallurgy in 2018.

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https://urn.fi/URN:ISBN:978-952-60-8588-3

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[diss] Insinööritieteiden korkeakoulu / ENG