Integrating photogrammetry and discrete fracture network modelling for improved conditional simulation of underground wedge stability

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.

The Evolution of Geotech - 25 Years of Innovation

“…The DFN model inputs were derived using mine outcrop mapping data available. The P32 intensity (i.e., area of fractures per unit volume) was estimated using the P32 simulation method based on P21 (i.e., length of fractures per unit area) data derived from photogrammet ric surveys as described by (Rogers, et al, 2017). Fracture size parameters were determined from trace length statistical distributions and converted to an equivalent fracture radius using analytical methods provided by (Zhang, et al, 2002).…”

“…Figure 2 left diagram) results in an increasing mismatch between the pseudo tensile strength (x-axis) intersection point of the H-B envelope (labelled DTS-H-B in Figure 2 right diagram), with decreasing mi values, compared with the true DTS value, (as labelled DTS-FH). While DTS parameter discrepancies detracting from reliable H-B use have been a topic of concern, (e.g., , Bewick et al, 2017, due mainly to errors in numerical modelling prediction, this mismatch has also been clouded by uncertainties arising from lack of reliable tensile strength determinations. This is not a new issue, (Fairhurst, 1964), but unfortunately, there are few alternatives available for improv ing Brazilian test method reliability as an expedient solution, while work continues on alleviating the many problems involved with carrying out true Direct Tension tests.…”

A review of structural data collection methodologies for discrete fracture network generation

“…Dershowitz & Herda (1992a) defined a system of fracture intensity measures of different dimensions applicable to a special joint set distribution. This is summarised and modified by Rogers et al (2017) in Table 1. Fracture intensity measures are referred to as Pij, where the subscript i refers to the dimensions of a sample and the subscript j refers to the dimension of measurement.…”

“…• Underground stability analyses (e.g. Esmaieli et al 2010Esmaieli et al , 2013Grenon & Hadjigeorgiou 2003;Grenon et al 2015;Rogers et al 2017).…”

Structural recognition and rock mass characterization in underground mines: A UAV and LiDAR map ping bas ed appr oach

“…This technique is the process of constructing maps or 3D models of real world objects or scenes of underground tunnels and caves based on distance measurements from photographs. Currently, this remote sensing method is used in mining for stability studies and structural mapping, specifically development of Discrete Fracture Network (DFN) models across all the sections with exposure of rock faces (Benton et al 2017;Rogers et al 2017;Lato et al 2013). Photogrammetry has some clear advantages over traditional field sampling techniques: it is possible to perform it at a safe distance from hazardous conditions and it can generate a permanent geometric record for future analysis.…”