255 / 2018-07-31 17:10:35
Analysis on the classification model of coal’s bursting liability based on database with large samples
large sample; coal’s bursting liability; classification; index relevance; distance discriminant analysis; comprehensive evaluation
全文录用
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 was 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 dynamic failure time, elastic energy index, impact 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 of DDA model reached 96%. The results of 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 distance discrimination method is both accurate and easy to calculate, and the DDA model has good engineering application prospect.
重要日期
  • 会议日期

    10月22日

    2018

    10月24日

    2018

  • 05月31日 2018

    摘要截稿日期

  • 07月05日 2018

    初稿截稿日期

  • 08月10日 2018

    初稿录用通知日期

  • 10月24日 2018

    注册截止日期

主办单位
北京科技大学
McGill University
中国矿业大学(北京)
河南理工大学
University of Wollongong
东北大学
重庆大学
中国矿业大学
Laurentian University
辽宁工程技术大学
西安科技大学
北方工业大学
江西理工大学
黑龙江科技大学
协办单位
中国职业安全健康协会
中国安全生产科学研究院
煤炭信息研究院
中安安全工程研究院
International Journal of Mining Science and Technology
Safety Science
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