Study on rockburst mechanism and risk identification for isolated working face in extra-thick coal seam fold zone

Faced with the challenges of high in-situ stress and elevated rockburst risk in isolated working faces within fold zones, this study takes an isolated working face in a specific coal mine as the engineering background. By integrating theoretical analysis, numerical simulation, and field practice, we investigated the evolutionary characteristics of overlying strata structure, clarified the variation law of mining-induced stress, revealed the rockburst mechanism of isolated working faces in fold zones of extra-thick coal seams, and established a dynamic risk identification model for the working face.The results show that when the minimum width of the coal pillar (95 m) during mining in the fold zone is significantly larger than the critical width (36.5 m), the overlying strata of the working face form a unique long-arm "F" structure. The cantilever effect of this structure causes the peak stress of the coal-rock mass in the working face advance area to increase to 42.1 MPa, with a stress concentration factor of 2.34. The superposition of the high static load in the advance area and the intense dynamic load released by the fracturing of the "F" structure leads to a significant decrease in the minimum principal stress and a corresponding increase in the maximum principal stress. This pushes the coal-rock mass beyond its strength limit, ultimately triggering a rockburst.To address the limitation of low early-warning accuracy (81.4%) associated with traditional single-index methods, a multi-index collaborative identification model based on microseismic data was constructed. Utilizing a Back-Propagation (BP) neural network algorithm, the model adaptively optimizes the coupling weights of six key indicators: b-value (0.04), P(b)-value (0.009), ∆F-value (0.185), AC-value (0.218), EEM-value (0.256), and S-value (0.292). This realizes a paradigm shift from "single-index alarm" to "multi-index collaborative discrimination". Field validation demonstrates that the model improves the early-warning accuracy to 94.9%, significantly enhancing the capability for dynamic risk identification of rockbursts in the working face. The research conclusions provide a valuable reference for the safe mining of isolated working faces under similar geological and mining conditions.