Geotechnical and Geological Engineering

, Volume 32, Issue 3, pp 657–671 | Cite as

Geotechnical Risk Assessment of Mine Development Intersections with Respect to Mining Sequence

  • Wael Abdellah
  • Hani S. Mitri
  • Denis Thibodeau
  • Lindsay Moreau-Verlaan
Original paper


Mine developments such as haulage drifts and their intersections with cross-cuts are the only stope access in sub-level stoping mining system. Thus, they must remain stable during their service life. Haulage drift instability could lead to serious consequences such as: production delay, damage to equipment, loss of reserves and high operational cost. The goal of this paper is the stability of mine developments with respect to mining sequence with focus on the performance of haulage drift intersection during the production plan. A case study, the #1 Shear East orebody at Vale’s Garson Mine in Sudbury Ontario will be examined in this paper. A three-dimensional, elastoplastic, finite difference code (FLAC3D) is used for this study. The extent of strength-to-stress ratio corresponds to Mohr–Coulomb strength-to-stress ratio of 1.4 is used as failure evaluation criterion. The unsatisfactory performance is reached when the extent of strength-to-stress ratio exceeds the anchorage limit of the rockbolt. Stochastic analysis; adopting point-estimate method, is then employed with the numerical modelling to tackle the inherent uncertainty associated with rockmass properties. Then, the probability of instability at various mining steps is estimated for the roof and north wall of the studied intersection. The cost of consequence models is introduced to provide an economical solution if the intersection failed, blocked or damaged. Furthermore, the geotechnical risk associated with the instability of mine development intersection is estimated using risk-indexing tool. The results are presented and categorized in terms of probability, cost of consequence and risk-index at various mining stages.


Sub-level stoping mining Point-estimate method (PEM) Cost of consequences Case study and risk-indexing tool 

List of Symbols




Unit weight


Poisson’s ratio


Modulus of of elasticity


Friction angle


Dilation angle


Tensile strength


Geological strength index


Rock tunnelling quality index


Coefficient of variation = \(\frac{\upsigma}{\upmu}\)


Mean or average value or rockmass


Standard deviation


Major principal stress (compressive stresses are taken as negative)


Intermediate principal stress


Minor principal stress (vertical stress = γ·H)


Performance function


Resistance (represents rockmass strength)


Action (represents mining induced-stress)

f (X)

Joint probability density function of the vector (X)


Probability of unsatisfactory performance


Probability density function


Cost of consequence



This work is financially supported by a research grant from the Natural Sciences and Engineering Research Council of Canada in partnership with Vale ltd. The authors are grateful for their support.


  1. Abdellah W, Mitri HS, Thibodeau D, Moreau-Verlaan L (2012) Stochastic evaluation of haulage drift unsatisfactory performance using random Monte–Carlo simulation. Int J Min Miner Eng 4(1):63–87CrossRefGoogle Scholar
  2. Abdellah W, Mitri HS et al (2013) Stochastic stability analysis of mine developments with respect to planned mining sequence. Int J Rock Mech Min Sci Submitt Google Scholar
  3. Bawden WF, Tod J, Lausch P, Davison G (2002) ‘The use of geomechanical instrumentation in cost control in underground mining’. 1st deep and high stress mining seminar. Perth, Western AustraliaGoogle Scholar
  4. Bewick RP, Valley B (2009) Global approach to managing deep mining hazards. ROCKENG09: Diederichs M, Grasselli G (eds) Proceedings of the 3rd CANUS rock mechanics symposium, Toronto, May 2009Google Scholar
  5. Charette F (2012) ‘Monitoring of dynamic bolts for high stress mining’. Paper presented at In: Proceedings of 21st Canadian rock mechanics symposium, RockEng12Google Scholar
  6. Christian JT, Baecher BG (1999) Point estimate method as numerical quadrature. J Geotech Geoenviron Eng 125(9):779–786CrossRefGoogle Scholar
  7. Golder Associate and MIRARCO (2008). Draft report on: Garson mine geomechanical study 12Google Scholar
  8. Hoek E, Brown ET (1980) Empirical strength criterion for rock masses. J Geotech Eng Div ASCE 106(GT9):1013–1035Google Scholar
  9. ITASCA, Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D),user’s manual ver. 4.0, Itasca Consulting Group Inc, Minneapolis, Minnesota 55401 USA, 2009Google Scholar
  10. Kwangho Y, Yeonjun P et al (2005) Risk analysis for determination of a tunnel support pattern. Tunn Undergr Space Technol 20:479–486CrossRefGoogle Scholar
  11. Maloney S, Cai M (2006) In situ stress determination, Garson Mine, Project Report: 06-015, Geomechanics Research Center, MIRARCOGoogle Scholar
  12. McKinnon SD (2001) Analysis of stress measurements using a numerical model methodology. Int J Rock Mech Min Sci (IJRMMS) 38:699–709CrossRefGoogle Scholar
  13. Musunuri A, Wei W, et al. (2009) Assessment of drift stability using probability of failure. In: Mehrotra A, FK, HG, Singhal RK, Banff AB (eds) Proceedings of the eighteenth international symposium on mine planning and equipment selection. pp 987–996Google Scholar
  14. Perman F, Sjöberg J, et al. (2011) Detailed three-dimensional stress analysis of complex orebody geometry–model setup and results for the Malmberget mine. In: Continuum and distinct element numerical modeling in geomechanics Itasca international inc, MinneapolisGoogle Scholar
  15. Schweiger HF, Thurner R (2007) Basic concept and applications of point estimate methods in geotechnical engineering. pp 97–112Google Scholar
  16. Shnorhokian S, Mitri HS, Thibodeau D (2014) Numerical simulation of pre-mining stress field in a heterogeneous rock mass. Int J Rock Mech Min Sci. doi: 10.1016/j.ijrmms.2013.12.002 Google Scholar
  17. Vale Inco Limited—Garson Deep Pre-Feasibility Study/FEL 2. Geotechnical Report. Feb 2009Google Scholar
  18. Valley B, Kaiser PK, Duff D (2010) Consideration of uncertainty in modelling the behaviour of underground excavations”. In: Van Sint Jan M, Potvin Y (eds) Deep mining: 5th international seminar on deep and high stress mining, Santiago, Chile. Australian Centre for Geomechanics. pp 423–436Google Scholar
  19. Wei W, Mitri HS, Kelly C (2012) ‘Evaluating immediate mining induced ground movement the performance of the primary support system’. Paper presented at In: Proceedings of 21st Canadian rock mechanics symposium, RockEng12Google Scholar
  20. Zhang Y, Mitri HS (2008) Elastoplastic stability analysis of mine haulage drift in the vicinity of mined stopes. Int J Rock Mech Min Sci (IJRMMS) 45:574–593CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wael Abdellah
    • 1
  • Hani S. Mitri
    • 1
  • Denis Thibodeau
    • 2
  • Lindsay Moreau-Verlaan
    • 2
  1. 1.Department of Mining and Materials Engineering, Faculty of EngineeringMcGill UniversityMontrealCanada
  2. 2.Vale LtdSudburyCanada

Personalised recommendations