Development of a predictive model for failure risk in underground mine tunnels

Abstract

Underground mine tunnel failures pose severe safety and operational challenges, particularly in structurally complex regions such as the Greenstone Belt of Tiira, Uganda. These failures are commonly driven by poor geotechnical design, unassessed rock properties, and excessive overburden stress, often resulting in injuries, fatalities, equipment loss, and halted operations. To address this, the study aimed to develop a predictive model to assess tunnel failure risks and improve the design of underground mines. Field and laboratory data were collected from both failed and stable tunnel zones, focusing on parameters such as rock stress, specific gravity, tunnel dimensions, and overburden pressure. Laboratory analysis included uniaxial compressive strength testing. Statistical tools including Kendall’s Tau and Spearman’s rank correlation were employed to identify key predictors. A machine learning model, specifically a Random Forest Classifier (RFC), was trained on the labeled dataset and evaluated using performance metrics like accuracy, precision, recall, and AUC. The RFC model achieved high predictive performance: 90.5% accuracy, 92.3% precision, and an AUC of 0.99. Sensitivity analysis revealed rock strength, specific gravity, and overburden stress as the most influential variables affecting tunnel stability. Conversely, wider tunnel dimensions were associated with increased failure risk. The study demonstrates the potential of integrating machine learning into geotechnical engineering to predict tunnel failures effectively. This approach supports data-driven design in early-stage mine planning and enhances operational safety. It also contributes to sustainable mining practices aligned with SDG 8 and SDG 9.