Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Abstract 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.