Authors: Montiel, E; Varona, P; Fernandez, C; Espinoza, Z

Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2025_60

Cite As:
Montiel, E, Varona, P, Fernandez, C & Espinoza, Z 2020, 'Use of discrete fracture networks in 3D numerical modelling for stability analysis in open pits', in PM Dight (ed.), Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 913-926, https://doi.org/10.36487/ACG_repo/2025_60

Download citation as:   ris   bibtex   endnote   text   Zotero


Abstract:
On some occasions, structural mapping in open pit mining presents complications that do not allow for easy data collection. This causes omissions in the consideration of discontinuities (faults, fractures, joints, etc.) whose presence can be of vital importance for the stability conditions presented by the excavation itself. Although many academic articles have based their research on the use of discrete fracture networks (DFN) and how to estimate the joint persistence in a blocky system, projection depths of structures remain ambiguous, even with the information from surveys or drilling. Still, with the interpretation of an experimented mapper, the projection of structure depths could present limitations that would prevent the formation of failure mechanisms that may occur as the project progresses. In order to perform analyses that solve this problem, it has been found that the use of DFNs is considered a tool that allows the incorporation and assessment of the impact of geometric variations of the different structural systems being mapped, thus giving the opportunity to estimate the characteristics of importance in the structural systems as well as their involvement at depth. In order to present the results obtained with the use of these techniques, this article aims to demonstrate that the use of DFNs incorporated stochastically in numerical modelling at the mine scale, along with the method of elements distinctions, this could answer the questions regarding the structural condition of geology in the mine. Through the identification of failure mechanisms, this document intends to show the results obtained in a stochastic 3D evaluation where the use of DFN allows the determination of different geometrical characteristics of the pit and their stability conditions, providing solutions in the definitions of warnings in field monitoring and the validation of the project as it develops.

Keywords: slope stability, discrete fracture networks, failure mechanism, stochastic analysis

References:
Coates, DF 1981, Rock Mechanics Principles, Energy, Mines and Resources Canada, Monograph.
Dershowitz, WS, Lee, G, Geier, J, Foxford, T, La Pointe, P & Thomas, A 2011, FracMan – Interactive Discrete Feature Data Analysis, Geometric Modelling and Exploration Simulations: User Documentation, version 7.4, Golder Associates, Seattle.
Fisher, R 1953, Dispersion on a sphere, Royal Society London, London, pp. 295–305.
Hoek, E 2012, Blast Damage Factor D, RocNews,
Hoek, E, Carranza, C & Corkum, B 2002, ‘Hoek-Brown failure criterion-2002 edition’, Proceedings of the 5th North American Rock Mechanics Symposium and the 17th Tunnelling Association of Canada Conference, University of Toronto, Toronto,
pp. 267–273.
Mathis, JI 2016, ‘Structural domain determination - practicality and pitfalls’, in PM Dight (ed.), Proceedings of the First Asia Pacific Slope Stability in Mining Conference, Australian Centre for Geomechanics, Perth, pp. 203–212,
Mauldon, M & Dershowitz, WS 2000, ‘A multi-dimensional system of fracture abundance’, Geological Society of America Annual Meeting: Abstracts with Programs, vol. 32, issue 7.
Priest, SD 1993, Discontinuity Analysis for Rock Engineering, Chapman and Hall, London.
Read, J 2009, ‘Slope design methods’, in J Read & P Stacey (eds), Guidelines for Open Pit Slope Design, CSIRO Publishing, Collingwood.
Read, J & Stacey, P 2009, Guidelines for Open Pit Slope Design, CSIRO Publishing, Collingwood.
Starzec, P & Andersson, J 2002, ‘Probabilistic predictions regarding key blocks using stochastic discrete fracture
networks – examples from a rock cavern in south-east Sweden’, Bulletin of Engineering Geology and the Environment, vol. 61, pp. 363–378.
Terzaghi, RD 1965, ‘Sources of error in joint surveys’, Geotechnique, vol. 15, pp. 287–304.




© Copyright 2020, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
Please direct any queries or error reports to repository-acg@uwa.edu.au