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Neural Computing and Applications

, Volume 31, Issue 12, pp 8185–8193 | Cite as

Classified prediction model of rockburst using rough sets-normal cloud

  • Ran Liu
  • Yicheng Ye
  • Nanyan HuEmail author
  • Hu Chen
  • Xianhua Wang
Machine Learning - Applications & Techniques in Cyber Intelligence

Abstract

In view of complexity and uncertainty of problems in the prediction of rockburst, a classified prediction model of rockburst using rough sets-normal cloud is established. Seven main influencing factors including uniaxial compressive strength of rock σc, uniaxial tensile strength of rock σt, maximum tangential stress of surrounding rock σθ, rock integrality coefficient kv, ratio between uniaxial compressive strength of rock and uniaxial tensile strength of rock σc/σt, ratio between maximum tangential stress of surrounding rock and uniaxial compressive strength of rock σθ/σc and rock elastic deformation energy index wet are selected as the evaluation index of rockburst. Sixteen groups of rockburst examples at home and abroad are used as model construction samples. Then, the weight value of evaluation index of rockburst is obtained by rough sets and fuzzy sets. According to normal cloud theory and classification standard of rockburst, the cloud maps of evaluation index of rockburst are generated. Based on the normal cloud generator and sample data, the evaluation index of the classified sample is determined, and the comprehensive determination of evaluation index of the classified sample is obtained by combining the weight of the evaluation index of rockburst. Finally, the rockburst level is identified according to the principle of maximum membership degree. The classified prediction model of rockburst is used to predict five groups of rockburst samples at home and abroad, and the rockburst classification is coincident with the actual situation. The results show that the classified prediction model of rockburst using rough sets-normal cloud has great practicability.

Keywords

Classification of rockburst Fuzzy sets Rough sets Normal cloud Degree of certainty 

Notes

Acknowledgements

This work was funded by the study on the “Five High” Risk Identification and Control System of Enterprises (No. 149hubei-0002-2017AQ) and Hubei Province Safety Production Special Fund Project (2017HBAQZX).

Compliance with ethical standards

Conflict of interest

None.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Ran Liu
    • 1
  • Yicheng Ye
    • 1
    • 2
  • Nanyan Hu
    • 1
    Email author
  • Hu Chen
    • 1
  • Xianhua Wang
    • 3
  1. 1.School of Resource and Environmental EngineeringWuhan University of Science and TechnologyWuhanChina
  2. 2.Industrial Safety Engineering Technology Research Center of Hubei ProvinceWuhanChina
  3. 3.Sinosteel Wuhan Safety and Environmental Protection Research Institute Co., Ltd.WuhanChina

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