煤冲击倾向性分类模型的指标无量纲化影响研究

doi:10.16265/j.cnki.issn1003-3033.2019.11.004

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (11): 20-25.doi: 10.16265/j.cnki.issn1003-3033.2019.11.004

煤冲击倾向性分类模型的指标无量纲化影响研究

王超^1,2 讲师, 李岳峰¹, 张成良^**1,2 副教授, 刘磊^1,2 副教授, 黄许超¹

1 昆明理工大学国土资源工程学院,云南昆明 650093;
2 云南省中-德蓝色矿山与特殊地下空间开发利用重点实验室,云南昆明 650093

收稿日期:2019-08-09 修回日期:2019-10-13 发布日期:2020-10-30
通讯作者: ** 张成良(1978—),男,云南曲靖人,博士,副教授,主要从事岩土工程方面的研究。E-mail:zclky78@163.com。
作者简介:王超 (1984—),男,山东济宁人,博士,讲师,主要从事矿山安全及岩石力学方面的研究。E-mail:chaobest@163.com。
基金资助:
国家自然科学基金资助(51934003);云南省高校深地资源开发科技创新团队支持计划项目;云南省大学生创新创业训练计划项目(201810672016);昆明理工大学分析测试基金资助(2017T20130130)。

Study on influence of indicator dimensionless on classification model of coal's bursting liability

WANG Chao^1,2, LI Yuefeng¹, ZHANG Chengliang^1,2, LIU Lei^1,2, HUANG Xuchao¹

1 Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China;
2 Yunnan Key Laboratory of Sino-German Blue Mining and Utilization of Special Underground Space, Kunming Yunnan 650093, China

Received:2019-08-09 Revised:2019-10-13 Published:2020-10-30

摘要/Abstract

摘要： 为准确评判煤的冲击倾向性类别,并解决国家标准中的模糊综合评判方法难以判别 8种样本的难题,引入Bayes判别分析方法,选取动态破坏时间、弹性能量指数、冲击能量指数和单轴抗压强度作为评判指标,将110组冲击倾向性实测数据作为训练样本,构建Bayes判别模型;采用 4种无量纲化方法处理评判指标的原始数据,建立对应的判别模型,并开展无量纲化方法对模型判别准确率的影响研究。结果表明:基于归一化法无量纲化处理的Bayes模型的判别准确率最高,达到98.2%,该模型应用在10个工程实例的判别结果与实际情况完全一致,而且避免了指标相关性对冲击倾向性分类结果的影响。

关键词: 冲击地压, 冲击倾向性, 指标相关性, 无量纲化, Bayes判别模型, 影响分析

Abstract: In order to evaluate the intensity level of the coal's bursting liability and to solve the difficult problem that fuzzy comprehensive evaluation method cannot distinguish the bursting liability of 8 kinds of coal samples, the Bayes discriminant analysis method was introduced for classification of coal's bursting liability. Firstly, the duration of dynamic fracture, elastic energy index, bursting energy index and uniaxial compressive strength were selected as classification indicators. Then the Bayes discriminant model was established by taking 110 groups of data of bursting liability as training samples. Finally, four dimensionless methods were used to process the original data of the evaluation index, and the corresponding discriminant model was established. The influence of dimensionless methods on the discriminant accuracy of Bayes model was also studied. The results show that the Bayes model based on normalized method has the highest accuracy, reaching 98.2%, that the classification results of 10 different engineering samples by Bayes model are in good agreement with actual situation, and that the proposed model can avoid the influence of indicator correlation on classification results of coal's bursting liability.

Key words: rockburst, bursting liability, indicator correlation, dimensionless, Bayes discriminant model, influence analysis

中图分类号:

X936

王超, 李岳峰, 张成良, 刘磊, 黄许超. 煤冲击倾向性分类模型的指标无量纲化影响研究[J]. 中国安全科学学报, 2019, 29(11): 20-25.

WANG Chao, LI Yuefeng, ZHANG Chengliang, LIU Lei, HUANG Xuchao. Study on influence of indicator dimensionless on classification model of coal's bursting liability[J]. China Safety Science Journal, 2019, 29(11): 20-25.

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煤冲击倾向性分类模型的指标无量纲化影响研究

Study on influence of indicator dimensionless on classification model of coal's bursting liability

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