Design of Cut-Out Distance for Underground Coal Gallery Using Time-Dependent Constitutive Model

The stability of underground coal galleries is critically influenced by time-dependent deformation behavior of surrounding rock masses, particularly in deep mining environments where long-term stress redistribution can lead to delayed failure. In continuous miner-based mining systems, determining an appropriate cut-out distance is essential to ensure productivity and safety, especially for weak rock mass. This study proposes a novel numerical–statistical framework for the optimal design of cut-out distance (COD) in room-and-pillar coal mining using continuous miners. A time-dependent viscoelastic-viscoplastic constitutive model was implemented in FLAC۳D to simulate roof deformation across varying geo-mining conditions, including gallery widths (۵ & ۶ m), depths (۱۰۰ to ۴۰۰ m), and COD values (۴ to ۱۲ m). The Coal Roof Index (CRI), a composite geotechnical classification parameter, was incorporated to evaluate roof integrity. Results from the numerical simulations were used to develop two empirical models, COD₁ for depths ≤ ۲۰۰ m and COD₂ for depths > ۲۰۰ m, via multivariate nonlinear regression. The models demonstrated high predictive accuracy, with R² values of ۰.۹۵ and ۰.۹۰, respectively. The results reveal a strong correlation between the cut-out distance and various influencing parameters, i.e., width, depth, and CRI classification. Statistical validation through t-tests and ANOVA confirms the significance and reliability of the proposed model. Both proposed models have been validated by two field cases of the Indian coal mine. Critical CRI thresholds were quantified for safe CODs, offering actionable insights for field implementation. The proposed design approach provides a robust framework for improving the safety and sustainability of underground coal mine development, particularly under weak roof conditions.

کلیدواژه ها:

Underground gallery ، Coal Roof Index (CRI) ، Cut-out distance ، Numerical method ، Multivariate nonlinear regression