The depletion of shallow ore deposits is pushing the mining industry toward deeper underground operations, where efficient rock breakage remains a critical challenge. This thesis develops a mainly geotechnical, data-driven blast design framework for sublevel stoping in Boliden’s Kankberg mine in northern Sweden. By combining established empirical methods with innovative computational approaches such as fuzzy logic, which handles uncertainty and variability much better and is ideal for subjective variables such as rock mass properties. This study enhances Lilly’s Blastability Index and integrates Measurement While Drilling (MWD) data as a real-time proxy for in situ rock behaviour to complement and indirectly validate traditional blastability assessments. Site-specific geological and geotechnical data, together with laboratory tests, inform the development and calibration of this blasting framework. The results demonstrate the potential of the proposed Fuzzy Blastability Index (FBI) and the MWD rock factor to guide theoretical opening slot adjustments, with the intention of optimising slot raise design and reducing overall
drilling costs. The methodology proposes a robust feedback loop between design and operations, contributing to a more scientific approach to underground drilling and blasting. Ultimately, this research offers a transferable approach for a next-generation blast design support tool in underground stope blasting. Although large-scale field validation is still required, the framework establishes a foundation for smarter, real-time design practices for the Kankberg mine.