Intelligent Early Warning and Sustainable Engineering Prevention for Coal Mine Shaft Rupture

Abstract

Shaft lifting is an important process of coal mining, and its integrity is a prerequisite for ensuring efficient mining. The non-mining-induced rupture of vertical shafts in coal mines, primarily caused by the consolidation settlement of overlying unconsolidated strata due to aquifer dewatering, poses a significant threat to mining safety. Accurately predicting such ruptures remains challenging due to the multicollinearity and complex interactions among multiple influencing factors. This study proposes a novel multiscale discriminant analysis model, termed the SDA-PCA-FDA model, which integrates Stepwise Discriminant Analysis (SDA), Principal Component Analysis (PCA), and Fisher’s Discriminant Analysis (FDA). Initially, SDA screened five principal controlling factors from nine original variables. Subsequently, PCA was applied to reorganize these factors into three principal components, effectively eliminating information redundancy. Finally, the FDA model was established based on these components. Validation results demonstrated that the SDA-PCA-FDA model achieved high correct classification rates of 96.43% and 91.67% on the training and testing sets, respectively, significantly outperforming traditional FDA, PCA-FDA, and SDA-FDA models. Applied to engineering practice in the Yanzhou Mining Area, the model successfully predicted the rupture risk of the main shaft, consistent with field observations. Furthermore, to achieve sustainable governance, the “Friction Pile Method” was proposed as a preventive measure. Numerical simulations using NM2dc software determined the optimal governance parameters: a pile height of 112.86 m, a stiffness coefficient of 0.9, and a pile–shaft spacing of 10 m. A comparative analysis incorporating techno-economic sustainability indicators confirmed the superior effectiveness and economic viability of the friction pile method over traditional approaches. This research provides a reliable, multiscale methodology for both the prediction and sustainable governance of non-mining-induced shaft rupture.