Development of a predictive, risk-based model to assess the effects of maintenance decisions on vertical mine shaft structures

Abstract

PURPOSE : The study addresses the challenge of effective long-term maintenance of the structures of vertical mine shafts. These structures face significant degradation over time due to corrosion, the impact of falling objects, and exposure to harsh environments with high humidity, chemical contamination, and poor ventilation. Current maintenance practices often prioritise short-term needs, neglecting the long-term consequences for structural integrity and operational sustainability. To bridge this gap, the research introduces a novel predictive risk-based maintenance decision-making model. DESIGN/METHODOLOGY/APPROACH : The model incorporates finite element analysis and Monte Carlo simulations to evaluate the failure modes caused by corrosion, fatigue and falling objects while accounting for uncertainties in degradation rates and impact probabilities. The analysis calculates the energy of falling objects and estimates corrosion rates based on environmental conditions, enabling accurate predictions of the remaining useful life (RUL) of critical steel components. This is combined with an Integrated Structural Inspection and Maintenance Management (iSIMM) system, which combines structural inspection data with Computerised Maintenance Management Systems (CMMS). ORIGINALITY/VALUE : This model enables informed decision-making, enhancing safety, reliability, and cost-efficiency in mining operations. The research’s novelty lies in the integration of predictive and risk-based maintenance strategies, offering new insights into managing mine shaft structural integrity whilst integrating quantitative FEA-derived damage models (for impact) with stochastic, inspection-driven lifecycle simulation as a key methodological that enables the transition from qualitative inspection to predictive, risk-informed planning. FINDINGS : The model is used in a case study of a South African gold mine and demonstrates the practical application, showcasing its ability to optimise maintenance planning, reduce life cycle costs, and extend the lifespan of mine shafts, and to quantify the cost-risk trade-off between different multi-year maintenance strategies, a decision-support feature often missing in practice.