Analysis of Prediction Model of Failure Depth of Mine Floor Based on Fuzzy Neural Network

Analysis of Prediction Model of Failure Depth of Mine Floor Based on Fuzzy Neural Network 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, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geotechnical and Geological Engineering Springer Journals

Analysis of Prediction Model of Failure Depth of Mine Floor Based on Fuzzy Neural Network

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Publisher
Springer International Publishing
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Terrestrial Pollution; Waste Management/Waste Technology; Civil Engineering
ISSN
0960-3182
eISSN
1573-1529
D.O.I.
10.1007/s10706-018-0591-y
Publisher site
See Article on Publisher Site

Abstract

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,

Journal

Geotechnical and Geological EngineeringSpringer Journals

Published: Jun 2, 2018

References

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