Dual Perspectives: Safety Assessment of Legacy Pillars via Numerical Simulation and Artificial Intelligence Techniques

The long-term stability of legacy pillars remains a critical challenge in mining engineering, as pillar collapse may threaten human safety, damage infrastructure, and complicate sustainable mine closure. Conventional empirical methods are often inadequate for addressing the complexity of heterogeneous rock masses and time-dependent deterioration. In this study, a dual-perspective framework is proposed by integrating finite difference method (FDM)-based numerical simulation with artificial intelligence (AI) techniques to improve the reliability of pillar safety assessment. FDM models are developed to analyze stress redistribution, deformation, and failure processes of pillars under varying depths, geometries, rock quality, and rock mechanics. In parallel, AI models are trained on datasets derived from numerical simulations to provide rapid predictions of pillar instability probability (Pf) with high computational efficiency. The complementary use of both approaches ensures cross-validation: FDM simulations provide mechanistic insights into pillar behavior, while AI models enhance predictive capability and account for uncertainties in geological conditions. The integrated framework demonstrates superior robustness and applicability compared to single-method approaches, offering a comprehensive tool for assessing legacy pillar safety. This research provides practical guidance for hazard mitigation, mine closure planning, and the development of monitoring strategies in sustainable mining engineering.