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The combination of coal precise mining and information technology in the new century is one of the important directions for the future development of coal mining. Taking the fully mechanized top coal caving condition of a thick coal seam in the 90,101 working face of Baoshan Yujing Coal Mine in Shanyin City, Shanxi Province as an example, the intelligent identification method of section coal pillar stability was studied. The load transfer law of overlying strata in the upper part of coal pillar was analyzed, and the coal pillar values of each index were obtained by using an empirical formula, mean impact value-genetic algorithm-BP neural network (MIV-GA-BP) simulation experiment, and finite difference algorithm. The Delphi index evaluation system was used to calculate the optimal value of the coal pillar. The results showed that the non-contact cantilevered triangle on the two wings of the coal pillar in the goaf reduced the stress on the coal pillar; according to the width of the coal pillar at 10 m, 14 m, 16 m, and 20 m, combined with the relationship between the plastic zone and the core zone of coal pillar, and the relationship between the stress field and the ultimate strength of coal pillar, the numerical simulation value of the coal pillar was determined. The MIV (mean impact value) characteristics screened out the influencing factors of coal pillar width in the section near the horizontal fully mechanized top coal caving face order of importance; the relative error between the predicted value and the expected value of the MIV-GA-BP simulation experiment was less than 5%, which has good stability for the multi-factor nonlinear coupling prediction problem; and the optimal value of the coal pillar was 16.03 m by the intelligent identification method of the coal pillar. When the 16 m pillar was used, the surrounding rock deformation of the roadway was small, and the control effect was good. The research results provide a theoretical basis and reference for the parameter determination of similar projects.

Study on Intelligent Identification Method of Coal Pillar Stability in Fully Mechanized Caving Face of Thick Coal Seam