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Characterization of discrete fracture networks and their influence on caveability and fragmentation

University of British Columbia

This thesis focuses on the use of Discrete Fracture Network (DFN) modeling to simulate rock masses with different characteristics by varying fracture spacing, persistence and dispersion, and assessing block instability without failure due to brittle fracture. The DFN method was used together with block theory to assess block volumes, characterize block shapes, evaluate block failure modes and estimate block size distributions in simulated ore bodies. A model was built to simulate block caving and run tests of specific rock mass parameters to evaluate their impact on caveability and fragmentation. The potential of the Block Shape Characterization Method (BSCM) for evaluating the block shape distribution within a rock mass was further confirmed, especially when used with the DFN method. The stability of the generated blocks was evaluated based on the factors of safety obtained from the FracMan stability analysis. The information gathered during modeling suggested that of the variables analyzed, fracture persistence has the largest influence on the generation of drawbell blocking block sizes. Qualitative similarities between the apparent block volume and the blockiness character were observed and confirmed previous studies. The results indicate that caveability in this model is most sensitive to changes in fracture spacing. This research indicates that DFN modeling has great potential for fragmentation evaluation and determination, caveability assessment, and investigating the factors influencing the caving process.

Thesis/Dissertation

application/pdf

2009-04-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/7592

Mining Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/