Evaluating destress blasting for rock fracture and rockburst prediction in deep level hardrock mining

Deep level hardrock mining faces increasing challenges from elevated in situ stresses and associated rockbursts. This study aims to develop a systematic framework for evaluating destress blasting effectiveness, with a focus on deep Swedish hardrock mines. The methodology integrates a critical review and adaptation of global best practices: advanced numerical modelling (static and dynamic simulations), field-based rock fracture monitoring using ground penetrating radar and borehole imaging, and application of rockburst prediction criteria including the strain energy storage coefficient (F), brittle shear ratio, and burst potential index. Geostatistical simulations, machine learning models, and industrial internet of things-based, real-time monitoring are proposed to enhance predictive accuracy, model calibration, and operational adaptability. Key findings show that effective destress blasting evaluation requires multi-modal integration of numerical outputs, field observations, microseismic trends, and uncertainty quantification, accounting for site-specific geological variability and dynamic stress redistribution. The study advances the field by proposing a predictive, feedback-driven evaluation framework tailored for deep Swedish mining conditions, capable of improving proactive rockburst risk management. It offers both practical tools for mining practitioners to optimise stress management, reduce seismic hazards, and enhance excavation safety, and academic foundations for future refinement of destress blasting models, geotechnical monitoring strategies, and adaptive design protocols. This research contributes to safer, more efficient deep mining operations by bridging gaps between theoretical modelling, empirical monitoring, and real-time, data-driven blast design optimisation strategies.