Το work with title Fracture frequency and block volume distribution in rock masses by Stavropoulou Maria, Xiroudakis Georgios is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
M. Stavropoulou and G. Xiroudakis, “Fracture frequency and block volume distribution in rock masses,” Rock Mech. Rock Eng., vol. 53, no. 10, pp. 4673–4689, Oct. 2020. doi: 10.1007/s00603-020-02172-6
https://doi.org/10.1007/s00603-020-02172-6
There are important potential engineering applications of a prediction model for rock mass block size distribution from linear fracture frequency measurements along scanlines on exposed rock walls along diamond drill cores, or along digitized borehole walls. These pertain to the characterization of rock masses in mining and geotechnical engineering projects either using rock mass classification systems or empirical equations for the reduction of rock mass parameters or numerical simulation codes. Another application is the resource estimation of decorative stone deposits using diamond drill coring or image analysis of borehole walls. In these cases, the resources are derived from the distribution of rock block volumes greater than a certain cut-off volume. It could be realized that such a prediction could lead apart from the evaluation of the measured decorative stone resources, also to the optimization of pit limits and the final estimation of proven reserves. Further, it could lead to optimization of block volume diamond wire cuts at the later stage of squaring of block volumes by comparing the final distribution after squaring with the original in situ block size distribution. Towards this aim and based on the results of a previous paper, we further elaborate: (a) on the composition of the method to characterize the fracture frequencies observed along diamond drill cores and (b) on the analytical prediction of the block volume distribution. Analytical model predictions are finally validated against actual production data from a white dolomitic marble quarry and they are compared with predictions of a more rigorous numerical simulation model based on a non-homogeneous Poisson process.