Rogers, SF, Bewick, RP, Brzovic, A & Gaudreau, D 2017, 'Integrating photogrammetry and discrete fracture network modelling for improved conditional simulation of underground wedge stability', in J Wesseloo (ed.), Deep Mining 2017: Proceedings of the Eighth International Conference on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, pp. 599-610, https://doi.org/10.36487/ACG_rep/1704_40_Rogers
(https://papers.acg.uwa.edu.au/p/1704_40_Rogers/)
Abstract: Over the last decade, the advantages of discrete fracture network (DFN) models over more conventional tools for key block stability analysis have become increasingly apparent. Without their need for a series of simplifying assumptions regarding the fracture system, rock wedge formation and excavation geometry, DFN’s ability to accurately capture the underground rock mass is clear. Coupled with the probabilistic consideration of block formation and joint strength parameters, they provide a valuable tool to the engineer for risk-based underground stability assessments.
However, recent changes in DFN technology have allowed a step change in modelling realism to be incorporated. A major improvement is the ability to generate DFN models directly conditioned to photogrammetric surveys so that the kinematic assessment is carried out on a structural description that accurately reflects the scanned location. This conditioned DFN model is embedded within an unconditioned stochastic description of the rock mass away from the scanned rock mass exposure, thus, providing a model that is constrained by the available geotechnical data (boreholes, scanning, trace mapping) but accurately conditioned to the key observed structures. The result is an ability to optimise excavation and ground support designs with a method that intelligently handles the natural heterogeneity imposed by the rock mass, combining what we see with what we know.

Keywords: discrete fracture network (DFN), kinematic stability, photogrammetry