Predicting rock fragmentation and blast-induced damage

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In mining, the stability of excavations is paramount for the safety of workers and achieving adequate rock fragment sizes can considerably increase productivity. Blasting is a common method used for rock fragmentation and excavation. Empirical guidelines for blast design are widely used, even if these involve a degree of uncertainty and, consequently, potential instabilities and inadequate fragmentation. Optimal blast results often require multiple iterations of blast design and this practice is costly and time-consuming. The size of the in-situ blocks formed by the intersection of natural joints in the rock mass is required to predict rock fragmentation. Discrete fracture networks (DFN) are 3D joint systems representing the distribution of in-situ block sizes. Previous research demonstrated the potential of DFNs, coupled with hybrid Finite/Discrete element methods, for the prediction of rock fragmentation in a block caving operation. This method has potential applications in blasting to obtain valuable knowledge of the role of in-situ joint networks in the overall blast outcome. The principal objective of this research is to develop predictive models for blast-induced damage and rock fragmentation using DFNs and numerical modelling tools. The secondary objectives are to establish a procedure for the field assessment of blast-induced fragmentation and damage and to provide practical recommendations regarding the optimal blast design. This project will benefit from the collaboration with mining companies that recognize the value of this research and agreed to provide access to mine sites. The state of the art DFN tool MoFrac will provide realistic fracture networks to determine the in-situ block sizes and blast-induced fracture intensity. Using DFNs as an input to numerical simulations of blast-induced fragmentation will preserve fracture properties through the modelling process. A comparison of the simulation results with field measurements will calibrate the prediction model. Previous research contributions in optimizing geotechnical data collection campaigns at mine sites are key aspects for the selection of reliable parameters for numerical modelling. General modelling practice often overlook the potential gaps in the collected data. A major contribution of this research is the development of DFN-based predictive models for blast-induced damage and fragmentation, with the main advantage of providing realistic fracture networks for numerical modelling. Another valuable research outcome is the development of practical, quantified and user-friendly recommendations based on rock properties and joint orientation. These will benefit engineers, blasters and mine management; and will increased safety for the personnel working on site. A better definition of the blast damage zone can lead to improved stability analyses. Greater control on rock fragmentation increases mine productivity, with easier and cheaper materials handling.