Abstract
The movement and failure of overlying strata induced by underground coal mining cause “three zones,” including the caving zone, the water-conductive fractured zone, and the sagging zone from the bottom up. For knowledge about the height of the water-conductive fractured zone, there has been no empirical or theoretical formulae for thick coal seam using fully mechanized longwall mining with sublevel caving. This paper presents a methodology of determining the height of the water-conductive fractured zone based on the radial basis function neural networks (RBFNN) model in MATLAB software. Before modeling, the relationship between the height of the water-conductive fractured zone and mining thickness, the lithologic character of the overburden and its composite structures, and workface parameters was studied. After that, 32 and 7 measured data were used as training and testing samples, respectively. It has been found that the average relative error is 6% and the maximum relative error is 10% for 7 test samples by comparing actual results with predicted results. The model was applied to the no. 31503 workface in the Gaozhuang coal mine for safety evaluation. The predicted value is 59.6 m, and the measured value is 55.9 m. The RBF-based model shows much better performance than empirical formulae in the Regulations for Coal Mining and Coal Pillar Design under Buildings, Water-bodies, Railways and Main Shafts for the prediction of the height of the water-conductive fractured zone for fully mechanized mining with sublevel caving.
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Acknowledgments
This research is financially supported by National Key Research Program of China during 13th Five Year (No. 2016YFC0801801), China National Natural Science Foundation (Grant No. 41572222, No. 41272276, No. 41430318, No. 51274135), Innovation Research Team Program of Ministry of Education (IRT1085). The authors would like to thank the scholars for their sample data in the field measurement. The authors would also like to thank the editor and the reviewers for their constructive suggestions.
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Wu, Q., Shen, J., Liu, W. et al. A RBFNN-based method for the prediction of the developed height of a water-conductive fractured zone for fully mechanized mining with sublevel caving. Arab J Geosci 10, 172 (2017). https://doi.org/10.1007/s12517-017-2959-3
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DOI: https://doi.org/10.1007/s12517-017-2959-3