Elmo, D & Stead, D 2020, 'Disrupting rock engineering concepts: is there such a thing as a rock mass digital
twin and are machines capable of learning rock mechanics?', in PM Dight (ed.), Slope Stability 2020: Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering
, Australian Centre for Geomechanics, Perth, pp. 565-576, https://doi.org/10.36487/ACG_repo/2025_34
Introduced by NASA engineers in the early 1960s during the Apollo space program, the term ‘digital twin’ has recently gained more visibility and exposure thanks to the development of the related new concept of the Internet of Things, IoT. A digital twin is not merely a model of a real physical asset but represents the actual connection between the physical world and the virtual (digital) reality. In this context, numerical models of a rock mass are virtual prototypes of that rock mass, which can be used to guide the design process and to estimate possible ground behaviour; they cannot however be considered true rock mass digital twins.
Rock engineering differs from other engineering disciplines since often design must be completed prior to developing access to rock exposures, forcing engineers and practitioners to rely on information obtained from severely limited 1D sampling methods. Even if engineers were to have unlimited resources, the natural variability of the rock mass, combined with the limited knowledge of the rock mass in the early phases of a project, would be such that the design outcome would still be influenced by what we do not know rather than by what we effectively know. The authors strongly believe that if initial 1D information to be complemented by 2D sampling during slope excavation, and smart sensors embedded within the rock mass, the opportunity exists to update virtual models and to compare models to the data from the smart sensors on a periodic basis. Note that the smart sensors would have to provide more information than deformation alone, since failure of intact rock bridges in engineered slopes is a progressive damage process and as such, requires location of the source where damage is accumulating.
This paper outlines the challenges facing the rock engineering community if we really want to truly transform and improve virtual (digital) geological and geomechanically rock mass models. The authors also provide a critical discussion on the potential use of machine learning algorithms based on empirical methods and the use of qualitative to semi-quantitative scales of measurements that are inappropriate for a full statistical analysis.
Keywords: machine learning, digital twin, rock engineering
Baczynski, NRP 2000, ‘Stepsim4 - Step-path method for slope risks’, Proceedings of the International Conference on Geotechnical and Geological Engineering, Melbourne.
Bieniawski, ZT 1989, Engineering rock mass classification, Wiley, New York.
Bilal, S, Negara, A & Shujath, S 2018, ‘Digital rock physics combined with machine learning for rock mechanical properties characterization’, Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference, Society of Petroleum Engineers, Abu Dhabi.
Call, RD & Nicholas, DE 1978, ‘Prediction of step path failure geometry for slope stability analysis’, Proceedings of the 19th US Symposium on Rock Mechanics, Mackay School of Mines, Reno.
Deere, DU, Hendron, AJ, Patton, FD & Cording, EJ 1967, ‘Design of surface and near surface construction in rock’, in C Fairhurst (ed.), Proceedings of the 8th U.S. Symposium on Rock Mechanics–Failure and Breakage of Rock, American Institute of Mining, Metallurgical and Petroleum Engineers, Inc., New York, pp. 237–302.
Dershowitz, WS, Finnila, A, Rogers, S, Hamdi, P & Moffitt, KM 2017, ‘Step path rock bridge percentage for analysis of slope stability’, Proceedings of the 51st U.S. Rock Mechanics/Geomechanics Symposium, American Rock Mechanics Association, Alexandria.
Einstein, HH, Veneziano, D, Baecher, GB & O’Reilly, KJ 1983, ‘The effect of discontinuity persistence on rock slope stability’, International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, vol. 20, pp. 227–236.
Elmo, D, Donati, D & Stead, D 2018, ‘Challenges in the characterization of rock bridges’, Engineering Geology, vol. 245, pp. 81–96.
Elmo, D, Moffitt, K, D’Ambra, S & Stead, D 2009, ‘A quantitative characterisation of brittle rock fracture mechanisms in rock slope failures’, in JR Read (ed.), Proceedings of the 2009 International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, University de los Andes, Santiago.
Grieves, J 2003, Strategic Human Resource Development, SAGE Publications Ltd, London, DOI:
Harrison, J 2017, ‘Rock engineering design and the evolution of Eurocode 7’, Proceedings of the EG50 Engineering Geology and Geotechnics Conference, International Society of Rock Mechanics, Portsmouth.
Hencher, SR, Lee, SG, Carter, TG & Richards, LR 2012, ‘Sheet joints: Characterization, shear strength and engineering’, Rock Mechanics and Rock Engineering, vol. 44, pp. 1–22.
Hoek, E 2007, Practical rock engineering, RocScience, Vancouver,
Jennings, JE 1970, ‘A mathematical theory for the calculation of the stability of slopes in open cast mines’, in PWJ Van Rensburg (ed.), Proceedings of the Symposium on the Theoretical Background to the Planning of Open Pit Mines, South African Institute of Mining and Metallurgy, Cape Town, pp. 87–102.
Li, T, Lee, X & Xiao-li, Y 2017, ‘Rock burst prediction based on genetic algorithms and extreme learning machine’, Journal of Central South University, vol. 24, issue 9, pp. 2105–2113.
Marcus, G 2017, Artificial Intelligence is Stuck. Here’s How to Move it Forward, New York Times, New York.
Mayer, J 2015, Applications of uncertainty theory to rock mechanics and geotechnical mine design, MSc thesis, Simon Fraser University, Burnaby.
Pu, Y, Apel, D, Liu, V & Mitri, H 2019, ‘Machine learning methods for rockburst prediction-state-of-the-art review’, International Journal of Mining Science and Technology, vol. 29, issue 4, pp. 565–570.
Read, J & Stacey, P 2009, Guidelines for Open Pit Slope Design, Taylor & Francis Ltd, London.
Read, JR & Lye, GN 1984, ‘Pit slope design methods: Bougainville copper open cut’, Proceedings of the 5th International Congress on Rock Mechanics, Rotterdam: AA Balkema, Melbourne, pp. C93-C98.
Ren, Q, Han, S & Lin, M 2018, Prediction of rock compressive strength using machine learning algorithms based on spectrum analysis of geological hammer’, Geotechnical and Geological Engineering, vol. 37, issue 1, pp. 475–489, ‘
Shang, J, Hencher, SR, West, LJ & Handley, K 2017, ‘Forensic Excavation of Rock Masses: A Technique to Investigate Discontinuity Persistence’, Rock Mechanics and Rock Engineering, vol. 50, pp. 2911–2928.
Stead, D, Elmo, D, Yan, M & Coggan, J 2007, ‘Modelling brittle fracture in rock slopes: experience gained, and lessons learned’, in Y Potvin (ed.), Proceedings of the 2007 International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 239–252.
Stevens, SS 1946, ‘On the theory of scales of measurement’, Science, vol. 103, issue 2684, pp. 677–680.
Taleb, N 2010, The Black Swan: The Impact of the Highly Improbable, Random House, 400 pp.
Terzaghi, K 1962, ‘Stability of steep slopes on hard unweathered rock’, Géotechnique, vol. 12, pp. 251–270.
Uzielli, M 2008, ‘Statistical data of geotechnical data’, in AB Huang & PW Maybe (eds), Geotechnical and Geophysical Site Characterisation, CRC Press, London.
Xu, H, Zhou, J, Asteris, PG, Armaghani, DJ & Tahir, MM 2019, ‘Supervised Machine Learning Techniques to the prediction of tunnel boring machine penetration rate’, Applied Science, vol. 9.
Zhang, Q & Song, J 1991, ‘The application of machine learning to rock mechanics’, Proceedings of the 7th ISRM Congress, International Society for Rock Mechanics and Rock Engineering, Aachen.
Zhou, J, Li, X & Mitri, H 2016, ‘Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods’, Journal of Computing in Civil Engineering, vol. 30, issue 5.