Machine learning framework application for modelling geomechanical instabilities: a caving case study

Authors: Quevedo, R; Sari, YA; McKinnon, S

Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2465_53

Cite As:
Quevedo, R, Sari, YA & McKinnon, S 2024, 'Machine learning framework application for modelling geomechanical instabilities: a caving case study', in P Andrieux & D Cumming-Potvin (eds), Deep Mining 2024: Proceedings of the 10th International Conference on Deep and High Stress Mining, pp. 853-866, https://doi.org/10.36487/ACG_repo/2465_53

Download citation as:   ris   bibtex   endnote   text   Zotero


Abstract:
The field of machine learning (ML) has had a significant impact on, and adoption in, many fields of science and engineering, yet in mining is still not very well developed, with many publications exploring scopes of application and potential areas of integration. As mining reaches deeper environments where most traditional methods of stability analysis have yet to be calibrated, there are good opportunities to apply ML methods to diverse types of, for example, failure phenomena. Still there is a necessity to properly account for adequate data inclusion and problem definition to apply these kinds of analysis, which is why data representation and availability with regards to a particular problem are crucial In this paper a case study of the application of data extraction and the ML modelling process applied to rock mass failure phenomena taking place in an underground cave mine is presented as an illustrative example of the practical application of ML methods of analysis in mining. The results show that ML methods have high potential in mining applications when coupled with careful consideration of input variables and the correct choice of ML approaches. The fundamentals and practical aspects are outlined such that the methodology of the case study is generalisable to different kinds of geomechanical problems.

Keywords: machine learning, mine design, caving mechanics, caving projects

References:
Beard, D & Brannon, C 2018, ‘Grasberg Block Cave mine: cave planning and undercut sequencing’, in Y Potvin & J Jakubec (eds), Caving 2018: Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 373–384,
Breiman, L 2001, ‘Random forests’, Machine learning, vol. 45, pp. 5–32.
Breiman, L 2017, Classification and Regression Trees, Routledge, London.
Carvalho, J, Santos, JPV, Torres, RT, Santarém, F & Fonseca, C 2018, ‘Tree-based methods: concepts, uses and limitations under the framework of resource selection models’, Journal of Environmental Informatics, vol. 32, no. 2.
Chimunhu, P, Topal, E, Ajak Duany, A & Asad, W 2022, ‘A review of machine learning applications for underground mine planning and scheduling’, Resources Policy, vol. 77, pp. 102693.
Cornejo, J & Pardo, C 2014, ‘Management indicators for the cave geometry control, El Teniente mine’, Caving 2014: Proceedings of the 3rd International Symposium on Block and Sublevel Caving, pp. 243–249, Universidad de Chile, Santiago.
Cuello, D & Newcombe, G 2018, ‘Key geotechnical knowledge and practical mine planning guidelines in deep, high-stress, hard rock conditions for block and panel cave mining’, in Y Potvin & J Jakubec (eds), Caving 2018: Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 17–36,
ACG_rep/1815_0.2_Cuello
Elmo, D, Cammarata,G & Brasile, S 2021, ‘The role of numerical analysis in the study of the behaviour of hard-rock pillars’, IOP Conference Series: Earth and Environmental Science, vol. 833, pp. 012132, IOP Publishing, Bristol.
Ferguson, GA, Cuello, D, Gandara, P, Potvin, Y & Rojas, E 2018, ‘Strategy for research and development in the cave mining industry’, in Y Potvin & J Jakubec (eds), Caving 2018: Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 487–498,
Flores, G & Catalan, A 2019, ‘A transition from a large open pit into a novel “macroblock variant” block caving geometry at Chuquicamata mine, Codelco Chile’, Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 3, pp. 549–561.
Gomes, ARA, Rojas, E & Ulloa, JC 2016, ‘Severe rock mass damage of undercut and extraction level pillars at El Teniente mine’, Geomechanics and Tunnelling, vol. 9, no. 5, pp. 529–533.
Hastie, T, Tibshirani, R & Friedman, JH 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, Berlin.
Heydari, S, Hoseinie, SH & Bagherpour, R 2024, ‘Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models’, Scientific Reports, vol. 14, no. 1.
Jing, L & Hudson, JA 2002, ‘Numerical methods in rock mechanics’, International Journal of Rock Mechanics and Mining Sciences, vol. 39, no. 4, pp. 409–427.
Jordan, MI & Mitchell, TM 2015, ‘Machine learning: trends, perspectives, and prospects’, Science, vol. 349, no. 6245, pp. 255–260.
Landeros, P, Cuello, D & Rojas, E 2012, ‘Cave-back management at Reservas Norte Mine, Codelco Chile, El Teniente Division’, Proceedings of MassMin 2012.
Lawal, AI & Kwon, S 2021, ‘Application of artificial intelligence to rock mechanics: an overview’, Journal of Rock Mechanics and Geotechnical Engineering, vol. 13, no. 1, pp. 248–266.
McGaughey, J 2020, ‘Artificial intelligence and big data analytics in mining geomechanics’, Journal of the Southern African Institute of Mining and Metallurgy, vol. 120, no. 1, pp. 15–21.
Morgenroth, J, Khan, UT & Perras, MA 2019, ‘An overview of opportunities for machine learning methods in underground rock engineering design’, Geosciences, vol. 9, no. 12.
Murphy, KP 2012, Machine Learning: A Probabilistic Perspective, MIT Press, Cambridge.
Nikolić, M, Roje-Bonacci, T & Ibrahimbegović, A 2016, ‘Overview of the numerical methods for the modelling of rock mechanics problems’, Tehnički Vjesnik, vol. 23, no. 2, pp. 627–637.
Paraskevopoulou, C, Perras, M, Diederichs, M, Loew, S, Lam, T & Jensen, M 2018, ‘Time-dependent behaviour of brittle rocks based on static load laboratory tests’, Geotechnical and Geological Engineering, vol. 36, pp. 337–376.
Pardo, C, Villaescusa, E, Beck, D & Brzovic, A 2012, ‘Back analysis of intensive rock mass damage at the El Teniente Mine’, 2012 Australian Mining Technology Conference.
Rudin, C 2019, ‘Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead’, Nature Machine Intelligence, vol. 1, no. 5, pp. 206–215.
Sherizadeh, T & Kulatilake, PHSW 2016, ‘Assessment of roof stability in a room and pillar coal mine in the US using three-dimensional distinct element method’, Tunnelling and Underground Space Technology, vol. 59, pp. 24–37.
Sinha, S & Walton, G 2019, ‘Numerical analyses of pillar behaviour with variation in yield criterion, dilatancy, rock heterogeneity and length to width ratio’, Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 1, pp. 46–60.
Song, G & Yang, S 2018, ‘Probability and reliability analysis of pillar stability in South Africa’, International Journal of Mining Science and Technology, vol. 28, no. 4, pp. 715–719.
Vásquez, P, Díaz Salas, J & Barindelli, G 2023, ‘Lecciones aprendidas, colapsos MB N01S02, Chuquicamata Subterránea’, Primer Congreso Chileno Mecánica de Rocas, pp. 940–947.
Wojtecki, Ł, Iwaszenko, S, Apel, DB, Bukowska, M & Makowka, J 2022, ‘Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings’, Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 3, pp. 703–713.
Zhou, J, Li, X & Mitri, HS 2015, ‘Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction’, Natural Hazards, vol. 79, pp. 291–316.
 




© Copyright 2024, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
View copyright/legal information
Please direct any queries or error reports to repository-acg@uwa.edu.au