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Research on the classification model of coal’s bursting liability based on database with large samples

  • Chao Wang
  • Dazhao SongEmail author
  • Chengliang Zhang
  • Lei Liu
  • Zonghong Zhou
  • Xuchao Huang
ISMSSE 2018
  • 20 Downloads
Part of the following topical collections:
  1. Mine Safety Science and Engineering

Abstract

In order to evaluate the intensity of the coal’s bursting liability scientifically and accurately, the index relevance problems existing in the current classification methods were focused on, and the Mahalanobis distance discriminant analysis (DDA) method was introduced on the basis of establishing a measured database with the large sample of the coal’s bursting liability. Besides, the DDA model of coal’s bursting liability classification was also established. Then, with the duration of dynamic fracture, elastic strain energy index, bursting energy index, and uniaxial compressive strength selected as evaluation indicators, three grade-discrimination functions of the bursting liability were established through training the data of 95 groups of bursting liability of different coal seams that were collected extensively. After training, the accuracy rate of the DDA model reached 96%. The results of the application of DDA model to the classification of coal samples from 10 coal mines exhibited remarkable agreement with the actual situation, which also solved the difficult problem that the fuzzy comprehensive evaluation method could not distinguish 8 kinds of samples. Its application to the engineering project shows that the classification result of coal’s bursting liability based on the DDA method is both accurate and easy to calculate, and the DDA model has good engineering application prospect.

Keywords

Large sample Coal’s bursting liability Classification Index relevance Distance discriminant analysis Comprehensive evaluation 

Notes

Funding information

This work is financially supported by the National Natural Science Foundation of China (51864023, 11862010), Program for Innovative Research Team (in Science and Technology) in University of Yunnan Province, College Students Innovation and Entrepreneurship Training Program of Yunnan Province (201810672016), the Analysis & Testing Foundation of Kunming University of Science and Technology (2017T20130130). The authors would like to express thanks to these foundations.

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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Chao Wang
    • 1
  • Dazhao Song
    • 2
    Email author
  • Chengliang Zhang
    • 1
  • Lei Liu
    • 1
  • Zonghong Zhou
    • 1
  • Xuchao Huang
    • 1
  1. 1.Yunnan Key Laboratory of Sino-German Blue Mining and Utilization of Special Underground Space, Faculty of Land Resource EngineeringKunming University of Science and TechnologyKunmingChina
  2. 2.School of Civil and Resources EngineeringUniversity of Science and Technology BeijingBeijingChina

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