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Purposes The intelligent filling of goaf is an important direction of green, safe, intelligent, and efficient mining of coal resources, and the key lies in intelligent decision-making and control of gangue-filling process in underground goaf. Methods To realize this, the stress and deformation of surrounding rock after filling are taken as monitoring indicators, and a deep neural network algorithm for intelligent filling in goaf is established, which can calculate and analyze the stope stress and surrounding rock deformation of different filling schemes under corresponding conditions by entering key basic parameters such as coal seam burial depth, thickness, working face length, and thickness of the direct roof. By using simulation results of FLAC3D under 400 different conditions as a dataset, the intelligent filling deep neural network algorithm was trained and tested, and compared with other three different algorithms. Findings The results show that: the intelligent filling deep neural network algorithm is generally better than the random forest algorithm, decision tree algorithm, and multiple linear regression algorithm, and the average caculation speed of each group of data is only 0.013 s; The average error values of key parameters such as the maximum deformation of the roof plate, the peak pressure of the coal wall of working face, and the advanced support distance of roadway calculated by the intelligent filling deep neural network algorithm are between 2%~8%; The algorithm is tested according to the actual conditions of the site, and the results are basically consistent with the actual results on the site, indicating that the algorithm is scientific and feasible. Conclusions This study is of great significance and value to green and intelligent mining. |
