Geotechnical risk management concept for intelligent deep mines
| Autor: | Mikael Rinne, Mateusz Janiszewski, Lauri Uotinen, Ritesh Mishra, Topias Siren, Martyna Szydlowska |
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| Přispěvatelé: | Department of Civil Engineering, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Fault tree analysis
Event tree inverse calculation Engineering Exploit business.industry ta1171 risk assessment 02 engineering and technology General Medicine 020501 mining & metallurgy rock stress 0205 materials engineering Work (electrical) Sustainability Geotechnical engineering business Raw data Risk assessment real-time data Risk management Deep underground hard rock mines |
| Zdroj: | Procedia engineering. 191:361-368 |
| ISSN: | 1877-7058 |
| Popis: | Deep mining, driven by the increasing need of the sustainable use of mineral resources, yields a chance to exploit untapped resources. Nevertheless, large depths remain challenging and complex environment, posing geotechnical risks such as stress driven damage. The violent damage mechanisms in deep mines are spalling and strainburst in their most severe forms. Real-time monitoring can not only assist in preventing a failure, but can also assist in post failure mitigations. It can help identify the possible systemic failure of adjacent areas and can therefore help in evacuating people and machinery from these areas. The long-term goal is to develop a real-time risk management concept for intelligent deep mines. The objective of this paper is to summarize the outcomes of I 2 Mine and DynaMine, formulate a risk concept suitable for real-time analysis and to produce a tangible measure of the risk levels. In this paper the Fault Tree – Event Tree methodology is proposed and an example is worked out using strainburst as an example risk case. The proposed methodology seems to work well and using a scenario with both property damage and ore loss, the risk expressed as financial consequences multiplied with probability drops from € 88,000 to € 11,000 corresponding to a - 80% reduction in risk. The financial consequences together with the associated risk level can be expressed visually using a modified FN graph with financial loss on x-axis and probability on the y-axis. The developed geotechnical risk management concept suits the need of semi-automated or fully automated risk management. It would fit well in the analysis stage of the raw data and would produce a stress state change, which could be used as input in the risk management chain for intelligent deep mines. |
| Databáze: | OpenAIRE |
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