Hamman, ECF, du Plooy, DJ & Seery, JM 2017, 'Data management and geotechnical models', in J Wesseloo (ed.), Proceedings of the Eighth International Conference on Deep and High Stress Mining
, Australian Centre for Geomechanics, Perth, pp. 461-487.
Shareholder value is driven by how well we, as the mining industry, can design, plan and mine. Well established reconciliation processes and reporting standards are utilised to ensure that the actual mining activities and the different planning cycles are effective and efficient for the specific life-of-mine plan. Its success is driven by the robustness of the specifications set out in the design. From a geotechnical perspective this means that there is a good understanding on how the different identified mechanisms can be controlled in a specific design option. In order to successfully control the mechanisms, an understanding of what drives the potential failure mechanisms and what can be considered as realistic amelioration options is required. This presents the geotechnical engineers with several challenges:
The solution begins with having reliable data. Geotechnical data is not very complex, however, the availability of suitable and accurate data, and quantum of data, drives the number of assumptions within a geotechnical design and thus the complexity thereof. One of the key challenges facing geotechnical engineers is the various forms and quality of geotechnical data available at operations and projects, in particular the more mature ones. The inherent uncertainty surrounding the data impacts how it can be evaluated and assessed. Assuming that the data is reliable, the geotechnical engineer faces a further challenge to complete a repeatable and auditable design. This starts with the processes and software used to evaluate and assess the data.
This paper deals with the building blocks leading up to the actual design, discussing frameworks to obtain reliable data and to assess the data. Ultimately, the authors aim to provide the reader with an insight into the frameworks being implemented in AngloGold Ashanti’s International Operations, which allow the practitioners to:
Keywords: data management, reliability, visualisation, rock mass model
Barton, NR, Lien, R & Lunde, J 1974, ‘Application of the Q-system in design decisions and appropriate support for underground installations’, in M Bergman (ed.), Proceedings of the International Conference on Subsurface Space, Pergamon Press, New York, pp. 553–561.
Basson, F 2017, GEM4D, BasRock, Perth, Western Australia, viewed 31 January 2017,
Bieniawski, ZT 1989, Engineering Rock Mass Classifications, Wiley, New York.
Cai, M, Kaiser, PK, Uno, H, Tasaka, Y & Minami, M 2004, ‘Estimation of rock mass deformation modulus and strength of jointed hard rock masses using the GSI system’, International Journal of Rock Mechanics and Mining Sciences, vol. 41, pp 3–19.
Davies, E 1967, Report of the Tribunal Appointed to Inquire into the Disaster at Aberfan on October 21st 1966, Her Majesty's Stationery Office, London, pp. 131–132.
Duran, A 2015, ‘A study in blockiness using borehole data for rock mass quality assessment’, Proceedings of the 2015 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, The Southern African Institute of Mining and Metallurgy, Johannesburg, pp. 841–852.
Duran, A 2016, ‘Rock mass assessment – what goes wrong?’, in P Dight (ed.), Proceedings of the First Asia Pacific Slope Stability in Mining Conference, Australian Centre for Geomechanics, Perth, pp. 493–506.
Harr, ME 1996, Reliability-based design in Civil Engineering, Dover Publications, New York.
Lilly, PA 2000, Risk Analysis and Decision Making, unpublished Master of Engineering Science course notes, Curtin University, Perth.
McMahon, BK 1978, Application of Rock Mechanics to Mine Design, Australian Mineral Foundation, Glenside, South Australia.
Norwegian Geotechnical Institute (NGI) 2015, Using the Q-system. Rock Mass Classification and Support Design, Norwegian Geotechnical Institute, Oslo, viewed 31 January 2016,
QGIS 2017, QGIS, version 2.18.3, QGIS, viewed 31 January 2017,
Read, JRL & Stacey, P 2009, Guidelines for Open Pit Slope Design, CSIRO Press, Collingwood, Victoria.
Rocscience Inc. 2016, Dips, Rocscience Inc., Toronto, Ontario.
Sandy, M, Sharrock, G, Albrecht, J & Vakili, L 2010, ‘Managing the transition from low stress to high stress conditions’, Proceedings of the Second Australasian Ground Control Conference in Mining, The Australasian Institute of Mining and Metallurgy, Carlton South, pp.247–253.
Sullivan, TD 2006, ‘Pit slope design and risk – a view of the current state of the art’, Proceedings of the International Symposium on Stability of Rock Slopes in Open Pit Mining and Civil Engineering, The Southern African Institute of Mining and Metallurgy, Johannesburg, pp. 51–78.
Wiles, TD 2006, ‘Reliability of numerical modelling predictions’, International Journal of Rock Mechanics & Mining Sciences, vol. 43, no. 3, pp. 454–472.
Wiles 2017, Map3D, Map3D International Ltd, viewed 1 February 2017, www.map3d.com
Ulusay, R & Hudson, JA 2007, The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 1974-2006, compilation arranged by the ISRM Turkish National Group, Ankara.
Ulusay, R & Hudson, JA 2016, The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 2007-2014, Springer, London.