Advanced DFN Models from Multi-Support Data for Underground Facilities. (2017)

• Record Type: Journal Article
• Authors: Le Goc, R.; Darcel, C.; Davy, P.
• Is Part Of: Procedia engineering. Volume 191(2017)
• Page Start: 1015
• Page End: 1022
• Publication Date: 2017
• Subjects: DFN -- Modelling -- Scale -- Borehole logs -- Fracture mapping; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.05.274
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Fractures have a significant impact on rock mass mechanical and hydraulic properties, which is a concern for rock engineering applications like excavation or repository design, support design, slope stability and caving in mines. To address this issue, a sound description of the fracturing pattern is required. DFN models are statistical models which define the density of fractures having given geometrical properties (size and orientation) and which include an intrinsic variability term. One of the main challenging task is to combine all available data. Data remain sparse and scarce and are acquired at different scales and from different support shapes and dimension (1D, 2D). We present a 3D modelling approach combining data from borehole logs, outcrop trace maps and tunnel walls mapping. It is applied to the Äspö site in Sweden, for which a large database is available, containing tens of thousands of records. Using stereological rules and assumptions about the underlying DFN scaling model, we are able to integrate all data to define the fracturing properties from the borehole scale (ten centimeters) to the repository scale (several kilometers). An advanced DFN modeling framework is applied, accounting for fractures mechanical interactions. This model has proved to be almost universal in crystalline rocks and reproduces, with very few parameters, the scaling properties of fractures. We show that this modelling framework better reproduces observations at all available scales and yields DFN, which structure and associated properties have a better consistency with natural cases than for simple DFN approaches.