Authors: Li, Y; Elmo, D

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DOI https://doi.org/10.36487/ACG_repo/2465_70

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Li, Y & Elmo, D 2024, 'Application of the network connectivity index on fragmentation assessment in cave mine design', in P Andrieux & D Cumming-Potvin (eds), Deep Mining 2024: Proceedings of the 10th International Conference on Deep and High Stress Mining, pp. 1091-1102, https://doi.org/10.36487/ACG_repo/2465_70

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Abstract:
Cave mine design relies on reasonable fragmentation assessment to optimise production efficiency and minimise operational costs. In the past decade the volumetric fracture intensity (P32) obtained from discrete fracture network (DFN) models has been widely used for fragmentation assessment in cave mine design. This paper examines the relationship between P32 and block sizes. The results show that P32 does not correlate well with key block size parameters D20, D50, and D80, which are sizes at 20, 50 and 80% mass passing, respectively. As an alternative, the network connectivity index 3D (NCI3D) is proposed as a geometric parameter to evaluate its correlation with block sizes. Results indicate that NCI3D exhibits stronger associations with D20, D50, and D80 compared to P32. Furthermore, NCI3D can be a computationally efficient alternative to the traditional DFNbased block formation approach for evaluating fragmentation characteristics in cave mine design. This parameter could be applied to mine-scale DFN models for assessing localised fragmentation within various locations of the orebody.

Keywords: network connectivity index, fracture intersection density, fracture, connectivity, discrete fracture network, fracture intensity, fragmentation assessment, cave mine

References:
Alghalandis, YF, Dowd, PA, & Xu, C 2015, ‘Connectivity field: a measure for characterising fracture networks’, Mathematical Geosciences, vol. 47, no. 1, pp. 63–83.
Bewick, RP & Elmo, D 2024, ‘Failure mechanism dependency of rock mass strength’, Rock Mechanics and Rock Engineering, under review.
Brown, E 2007, Block Caving Geomechanics, Julius Kruttschnitt Mineral Research Centre, The University of Queensland, Indooroopilly.
Chilès, J-P, Wackernagel, H, Beucher, H, Lantuéjoul, C & Elion, P 2008, ‘Estimating fracture density from a linear or aerial survey’, in J Ortiz & X Emery (eds), Proceedings of the VIII International Geostatistics Congress, Gecamin, Santiago, pp. 535–544.
Elmo, D 2006, Evaluation of a Hybrid FEM/DEM Approach for Determination of Rock Mass Strength Using a Combination of Discontinuity Mapping and Fracture Mechanics Modelling, With Particular Emphasis on Modelling of Jointed Pillars, PhD thesis, University of Exeter.
Elmo, D 2023, ‘The Bologna interpretation of rock bridges’, Geosciences, vol. 13, no. 2.
Elmo, D, Rogers, S, Stead, D & Eberhardt, E 2014, ‘Discrete fracture network approach to characterise rock mass fragmentation and implications for geomechanical upscaling’, Mining Technology, vol. 123, no. 3, pp. 149–161.
Elmo, D & Stead, D 2010, ‘An integrated numerical modelling–discrete fracture network approach applied to the characterisation of rock mass strength of naturally fractured pillars’, Rock Mechanics and Rock Engineering, vol. 43, pp. 3–19.
Elmo, D & Stead, D 2021, ‘The role of behavioural factors and cognitive biases in rock engineering’, Rock Mechanics and Rock Engineering, vol. 54, pp. 2109–2128.
Elmo, D, Stead, D, Yang, B, Marcato, G & Borgatti, L 2022, ‘A new approach to characterise the impact of rock bridges in stability analysis’, Rock Mechanics and Rock Engineering, vol. 55, no. 5, pp. 2551–2569.
Elmo, D, Yang, B, Stead, D & Rogers, S 2021, ‘A discrete fracture network approach to rock mass classification’, in M Barla, A Di Donna & D Sterpi (eds), Challenges and Innovations in Geomechanics: Proceedings of the 16th International Conference of IACMAG – Volume 1, Springer, Berlin, pp. 854–861.
Fogel, Y 2022, A Sensitivity Analysis for the Network Connectivity Index (NCI) Using Discrete Fracture Networks (DFN), PhD thesis, The University of British Columbia, Vancouver,
Hoek, E, Kaiser, PK & Bawden, WF 1995, Support of Underground Excavations in Hard Rock, CRC Press, Boca Raton.
Huang, F, Yao, C, Yang, J, He, C, Shao, Y & Zhou, C 2020, ‘Connectivity evaluation of fracture networks considering the correlation between trace length and aperture, Applied Mathematical Modelling, vol. 88, pp. 870–887.
ITASCA 2019, 3DEC (3 Dimensional Distinct Element Code), version 7.0, computer software, https://www.itasca.com.au/software/3dec
Jennings, J 1970, ‘A mathematical theory for the calculation of the stability of slopes in open cast mines’, in P van Rensburg (ed.), Planning Open Pit Mines: Proceedings of the Symposium on the Theoretical Background to the Planning of Open Pit Mines with Special Reference to Slope Stability, pp. 87–102.
Karimi Sharif, LK, Elmo, D & Stead, D 2019, ‘Improving DFN-geomechanical model integration using a novel automated approach’, Computers and Geotechnics, vol. 105, pp. 228–248.
Laubscher, D 1994, ‘Cave mining-the state of the art’, Journal of The Southern African Institute of Mining and Metallurgy, vol. 94, no. 10, pp. 279–293.
Marinos, V & Carter, TG 2018, ‘Maintaining geological reality in application of GSI for design of engineering structures in rock’, Engineering Geology, vol. 239, pp. 282–297.
Munkhchuluun, M 2017, Linking the Fracture Intensity of an In Situ Rock Mass to Block Cave Mine Fragmentation, PhD thesis, The University of British Columbia, Vancouver.
Ojeda, P, Elmo, D, Rogers, S & Brzovic, A 2023, ‘Discrete fracture network (DFN) analysis to quantify the reliability of borehole-derived volumetric fracture intensity’, Geosciences, vol. 13, no. 6,
Rasmussen, LL 2020, ‘UnBlocksgen: A Python library for 3D rock mass generation and analysis’, SoftwareX, vol. 12,
10.1016/j.softx.2020.100577
Rogers, S, Elmo, D, Webb, G & Catalan, A 2015, ‘Volumetric fracture intensity measurement for improved rock mass characterisation and fragmentation assessment in block caving operations’, Rock Mechanics and Rock Engineering, vol. 48, pp. 633–649.
Wang, X 2005, Stereological Interpretation of Rock Fracture Traces on Borehole Walls and Other Cylindrical Surfaces, PhD thesis, Virginia Tech, Blacksburg.
WSP 2021, FracMan, version 8.0, computer software, https://www.wsp.com/en-au/services/fracman
Xu, C, Dowd, P, Mardia, K & Fowell, R 2006, ‘A connectivity index for discrete fracture networks’, Mathematical Geology, vol. 38, pp. 611–634.
Zhang, X, Harkness, RM & Last, NC 1992,’ Evaluation of connectivity characteristics of naturally jointed rock masses’, Engineering Geology, vol. 33, no. 1, pp. 11–30.




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