Geotech Geol Eng https://doi.org/10.1007/s10706-018-0591-y ORIGINAL PAPER Analysis of Prediction Model of Failure Depth of Mine Floor Based on Fuzzy Neural Network . . Zhongchang Wang Wenting Zhao Xin Hu Received: 13 February 2018 / Accepted: 29 May 2018 Springer International Publishing AG, part of Springer Nature 2018 Abstract To obtain the law of failure depth of mine economic beneﬁt of mine, some measures and meth- ﬂoor and its inﬂuencing factors during coal mining ods through human intervention to reduce the failure process, a large amount of ﬁeld measured data of ﬂoor depth of ﬂoor and ensure mine safety were suggested. failure depth was collected, and ﬁve inﬂuencing factors were summarized based on the analysis of Keywords The failure depth of ﬂoor Fuzzy neutral data and years of ﬁeld experience. The ﬁve main network Inﬂuence factor Weight Prediction model inﬂuencing factors were the length of working face, mining depth, mining height, dip angle and ﬂoor anti- sabotage ability. Based on fuzzy math membership and membership function, the ﬁve factors were 1 Introduction preliminarily processed, then the sensitivity ranking was obtained according to the weight of inﬂuencing In recent years, with the gradual expansion of the factors,
Geotechnical and Geological Engineering – Springer Journals
Published: Jun 2, 2018
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