Geological and geotechnical challenges on the Great Dyke of Zimbabwe and their impact on hardrock pillar design

Abstract

The Great Dyke of Zimbabwe is a major geological formation renowned for its rich deposits of platinum group metals. This study addresses the geological and geotechnical challenges faced during mining on the Great Dyke, focusing on the implications for hardrock pillar design. The Great Dyke's geological complexity includes diverse rock types—dunites, harzburgites, pyroxenites, and norites—and notable structural features like joints, faults, and shear zones. These factors complicate the stability of underground workings. Traditional empirical methods and numerical modeling are used in pillar design but fall short in capturing the full complexity of the Great Dyke. The study highlights the absence of advanced methods such as machine learning (ML), artificial intelligence (AI), and geostatistical techniques in current pillar design practices. Incorporating these methods could significantly enhance pillar stability. Geostatistical techniques like kriging offer detailed estimates of rock quality and quantify prediction uncertainty, while ML and AI can analyze extensive data sets to uncover patterns and improve predictions. Integration of real-time data from Industrial Internet of Things sensors into these models allows for dynamic updates and better risk management. Continuous monitoring and adaptive design are essential for maintaining stability in this challenging geological environment. The study's findings aim to guide future mining practices, ensuring enhanced safety and efficiency on the Great Dyke.