Mine pillar design plays a crucial role in ensuring the stability and safety of underground mining operations, particularly in geologically and geotechnically complex settings like the Great Dyke of Zimbabwe. Traditional pillar stress determination methods, such as the tributary area method (TAM) and Coates’ method, have been widely applied in room and pillar mining. However, these approaches rely on simplifying assumptions—such as uniform load distribution, independent pillar behavior, and elastic deformation—which may not accurately capture the heterogeneous and anisotropic geotechnical conditions of the Great Dyke. This study critically revisits these methods, evaluating their limitations and proposing advanced alternatives for a more robust pillar design. The study observes that TAM oversimplifies stress distribution, leading to potential underestimations of stress concentrations in irregular pillar geometries and varying rockmass conditions. While Coates’ method improves on TAM by incorporating geometric parameters, it fails to account for overburden stiffness, seam interactions, and mining-induced stress redistribution. The study highlights the necessity of integrating real-time monitoring systems, site-specific numerical model calibration, and AI-driven predictive frameworks to improve pillar design reliability. The study enhances the understanding of stress redistribution, time-dependent failure mechanisms, and geological discontinuities that significantly impact pillar stability by critically reflecting on these computational approaches. It contributes to a deeper understanding of pillar stress determination on the Great Dyke, contributing to safer and more efficient mining operations. The study recommends a hybrid approach that merges traditional empirical techniques with advanced numerical modeling and machine learning, ensuring resilience against complex geological challenges while optimizing resource extraction and minimizing failure risks.

Deep underground hardrock mines face escalating rockburst risk as depth and stress increase. This thesis develops and demonstrates an integrated framework for stress management and rockburst mitigation that combines literature study based destress blasting findings and field trial based destress drilling findings with energy‐based indices observed from literature study, monitoring (specifically high-resolution 3D laser scanning and Cloud to Cloud analysis), and a conceptual geostatistics study, tailored to Swedish deep mining conditions. Four objectives structure the work: (i) based on literature study, derive a design framework for destress blasting from six decades of international and Swedish practice; (ii) based on the literature study, construct a quantitative evaluation methodology (Edestress concept contributed by this thesis work) that integrates the strain energy storage coefficient (F), brittle shear ratio (BSR) and burst potential index (BPI) with fracture and seismic observations; (iii) execute and analyse a controlled field trial of destress drilling at Zinkgruvan mine; and (iv) develop a geostatistical concept (semi-variograms and kriging) to predict destress efficiency at unsampled locations with quantified uncertainty. 

The methodology adopted for the thesis study integrates structured literature and case-history analysis, numerical and energy-based reasoning, and field experimentation with high-resolution 3D laser scanning of the drift and cloud-to-cloud (C2C) analysis. Based on literature study, the destress blasting component organises key rockmass, stress, and explosive parameters into a conceptual decision framework and design guidance; the evaluation framework developed from literature study specifies how F, BSR and BPI are computed and interpreted alongside monitored fracture and seismic responses; the Zinkgruvan mine practical field trial isolates the mechanical effect of uncharged inclined boreholes by keeping production-blast variables constant and quantifying geometric outcomes through C2C and volume-added metrics; and the geostatistical study shows how semi-variogram modelling and ordinary kriging can map performance indicators and their uncertainty to support risk-aware planning. 

Across case histories and supporting analyses, destress blasting is shown to be effective but highly localised and transient: stress relief typically extends only a few metres from the blast, and benefits decay rapidly as faces advance, necessitating continuous inclusion of destress features in each round within burst-prone zones. Mechanistic interpretation links reductions in boundary tangential stress and strain energy density to blast-induced fracture networks whose extent depends on rockmass brittleness and charging/timing choices; highly brittle rocks are both more burst-prone and more responsive when patterns and charge intensities are matched to site conditions. 

The practical Zinkgruvan mine field trial (depth of 1285 m) provides quantitative evidence that destress drilling stabilises development drifts. Rounds with 46 mm, 4 m destress drilling holes inclined at 20° (roof and shoulders) exhibited up to 2.5 m3 less scaled ‘volume added’ per metre of advance and a 20 – 30 % reduction in C2C profile standard deviation relative to non-destressed rounds, indicating lower overbreak and improved excavation profile control. It was observed that the first two rounds after a destressed round also performed comparably well, evidencing a short-range residual benefit that dissipates by the third non-destressed round, an operationally important finding for sequencing and cost-risk optimisation. 

The thesis study advances practice by: (i) organising destress blasting design considerations into a transferable, Swedish-context-aware framework; (ii) unifying energy indices (F, BSR, BPI) with fracture/deformation and seismic monitoring for quantitative evaluation at excavation scale; (iii) providing a high-fidelity, field-validated C2C/volume-based assessment of destress drilling in a deep, burst-prone Swedish mine; and (iv) introducing a geostatistical prediction concept that generates mine-wide efficiency maps with confidence bounds to reduce hazardous measurement campaigns and guide targeted data acquisition. 

The main conclusions and recommendations are that destress measures must be engineered and applied continuously in high-risk zones; design should be matched to rockmass brittleness and in situ stress; evaluation should jointly track energy indices, deformation/fracture, and seismicity; soft-scaling (where appropriate) practices should be integrated to minimise added volume; and geostatistical mapping and digital tools (3D scanning/C2C, IIoT) should underpin adaptive, feedback-driven planning.

The global transition to renewable energy and electrified transport depends on scalable, affordable, and secure battery storage technologies. Lithium-ion batteries (LIBs) currently dominate this sector due to their high energy density, long cycle life, and mature manufacturing infrastructure. However, increasing demand for lithium, cobalt, and nickel has exposed major supply chain vulnerabilities, including geographic concentration, refining bottlenecks, price volatility, environmental impacts, and social concerns associated with critical metal extraction. This study aims to critically compare lithium- and sodium-based battery technologies by examining their electrochemical fundamentals, critical material requirements, supply chain risks, environmental footprints, technical limitations, commercial readiness, and application-specific suitability. The study finds that sodium-ion batteries (SIBs) offer important strategic advantages due to the abundance and wide distribution of sodium, reduced dependence on cobalt and nickel, compatibility with aluminium current collectors on both electrodes, and potential cost and sustainability benefits in stationary and low-cost mobility applications. Key technical findings show that SIBs still face lower gravimetric and volumetric energy density, slower ion transport, hard carbon initial Coulombic efficiency losses, cathode phase instability, and electrolyte/interface challenges. Nevertheless, recent advances in hard carbon anodes, layered oxide cathodes, polyanionic frameworks, Prussian blue analogues, and electrolyte engineering are narrowing the performance gap. All in all, LIBs and SIBs are best understood as complementary technologies: LIBs will remain dominant in high-energy mobile applications, while SIBs are strategically positioned for grid storage, backup power, and micromobility, supporting a more resilient and diversified energy storage future.

Nigeria is endowed with vast solid mineral resources that remain largely underexploited, despite their potential to catalyse national infrastructure development and drive inclusive economic growth. This study examines the challenges and prospects of linking Nigeria’s mineral wealth with infrastructure development, within the broader context of sustainable development and regional integration. Drawing from empirical data, national reports, and comparative global case studies, the research identifies institutional fragmentation, policy and regulatory bottlenecks, weak governance, and limited financing mechanisms as key barriers to integrated mineral-infrastructure planning. It also explores successful models from countries such as Mozambique, Guinea, Botswana, Chile, and South Africa, providing strategic insights applicable to Nigeria’s context. The study proposes a framework for developing mineral corridors, embedding infrastructure obligations in mining licenses, leveraging resource-for-infrastructure partnerships, and enhancing inter-agency coordination. By implementing these reforms, Nigeria can unlock the transformative potential of its mining sector, bridge its infrastructure gap and foster long-term economic diversification. The findings contribute to policy discourse on resource-based development and offer actionable recommendations for stakeholders across government, industry, and development finance institutions.

Rockbursts are known for their unpredictable and violent nature, representing a significant threat to workers’ safety, mining productivity, and operational costs. Therefore, a quantitative assessment of rockburst damage is significant for geotechnical risk management in seismically active underground mines. Over the past few decades, numerous studies have been conducted to mitigate the risk posed by rockburst from various perspectives. Despite the scientific achievements and technological advances in ground control, rockburst still threatening underground mine operations because of the elusive character of the rockburst phenomenon and the challenges associated with its reliable prediction. Hence, the current study examines the possibility of implementing supervised machine learning algorithms to classify seismic events. Mining-induced seismicity pertaining to a deep gold mine exploiting the Witwatersrand Basin of South Africa was used to implement the models. The validation results showed the classification accuracy varied between 70 and 84% depending on the model implemented. These indicate good agreement with the seismic data and the induced rockburst events. It was concluded that the results of the study could assist in minimizing the risk of rockbursting in deep mines. 

Stope instability remains a persistent and hazardous challenge in underground mining, impacting safety, efficiency, and sustainability. Traditional stability assessment methods, while valuable, are often limited by site-specific calibration, simplifications, and adaptability issues in dynamic underground conditions. While machine learning shows potential for improved accuracy, a critical gap persists in understanding how geotechnical factors interact in practice. This study introduces a novel, practical machine learning framework (Scikit-Learn) to predict stope instability, and crucially, to quantify the nuanced, non-linear influence and interaction of critical geotechnical factors in a shallow gold mine. Comprehensive geotechnical investigation (observations, lab tests, rock mass classifications, blast damage assessments) and advanced data analysis (Random Forest feature importance, RFE, decision boundary analysis) identified water ingress, blast-induced damage, and rock mass quality (RMR) as the most significant instability factors. Water ingress profoundly impacted stability, with moderate blast damage exacerbating instability under high water ingress. Rock strength showed comparatively lower significance. The developed model achieved robust predictive performance (accuracy: 0.83, precision: 0.88, recall: 0.83, F1-score: 0.83). Based on these insights, tailored support patterns (e.g. 22mm/16mm cone bolts, timber props) are proposed to mitigate specific risks. This research significantly advances targeted rock mechanics solutions by providing a deeper, quantifiable understanding of complex instability mechanisms, enhancing mine safety and operational efficiency in shallow gold mining. 

This study advances pillar stress prediction and design optimisation in hardrock platinum mining on the Great Dyke of Zimbabwe using advanced machine learning (ML) techniques, addressing significant gaps in traditional methods. Utilising Gradient Boosting Machine (GBM), XGBoost, NGBoost, Random Forest, and AdaBoost, the research evaluated a dataset of 503 observed practical insitu pillars, incorporating key features such as Depth Below Surface (DBS), Actual Panel Width, and Actual Extraction Ratio (AER). GBM and XGBoost emerged as top performers, achieving R2 scores of 99.58% and 99.44%, respectively, with GBM exhibiting an MSE of 0.3094 and RMSE of 0.5563. NGBoost added value with predictive uncertainty, enhancing risk management frameworks. The study also highlights feature importance, emphasising DBS, AER, and Actual Pillar Area as critical predictors, ensuring robust and site-specific design solutions. Practical outcomes include a 15% reduction in material overdesign and a 20% improvement in identifying high-risk pillars, contributing to safer and more efficient operations. Integration with real-time monitoring systems enabled dynamic adjustments, reducing pillar failure risks by 30% under evolving conditions. This research, the first of its kind on the Great Dyke, demonstrates the transformative potential of ML in mining engineering, providing a framework for safer, economically viable, and sustainable operations. This study paves the way for leveraging ML to tackle complex geological and geotechnical challenges in global mining projects by addressing predictive accuracy and uncertainty.

This research aims to optimize ground support systems for underground tunnels in geologically challenging environments, specifically addressing the reduction of Fall of Ground (FOG) incidents in a gold mine in Mashava, Zimbabwe. The study integrates advanced detection and classification methodologies to enhance tunnel stability and safety. Tunnel Reflection Tomography (TRT) was employed to identify unfavorable geological structures ahead of excavation, while core logging at 20 locations on level 7 provided rock mass quality assessments using three classification systems: Bieniawski’s Rock Mass Rating (RMR), Laubscher’s Mining Rock Mass Rating (MRMR), and Barton’s Q-system. The results consistently indicated poor rock mass quality, informing the design and refinement of a robust ground support system. Fallout height data from past FOG incidents and probabilistic key block analysis using J-Block software further validated the support system's effectiveness. The findings significantly reduce collapse risks and downtime, enhancing operational safety and efficiency. This research contributes to developing practical strategies and tools for improving tunnel stability in complex geological settings, offering valuable insights for future advancements in mining support technologies. The study's necessity stems from the industry's growing demand for innovative solutions to enhance tunnel stability in adverse geological settings, particularly in regions with limited access to advanced technologies or methodologies.

As mining progresses to greater depths, the challenges of high stress become more pronounced, often resulting in rockbursts that significantly impact deep underground mining operations. To address these challenges, Zinkgruvan mine in Sweden is testing destress drilling as a proactive measure to reduce the propensity for rockbursts and enhance the long-term stability of the mining drift, particularly in the roof and shoulders. Destress drilling holes, in this study, were drilled at 20° inclination on the periphery of the exploration drift and strategically placed ahead of development blasts. Laser scans of the drift were conducted before and after scaling, and the point cloud data was analysed using Cloud Compare software, with the Cloud-to-Cloud (C2C) algorithm employed to detect profile changes. This allowed for a comparison between blast rounds with and without destress drilling to assess the technique’s effectiveness. Results demonstrated that destress drilling reduced stress concentrations in the surrounding rockmass, as evidenced by reduced profile change. Blast rounds with destress drilling had up to 2.5 m3 less volume added per metre. C2C analysis showed 20 % to 30 % lower standard deviation and consistently lower mean deviation, indicating improved profile uniformity. These findings highlight the technical and operational benefits of destress drilling.