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2013 , Vol. 30 >Issue 12: 54 - 59

DOI: https://doi.org/10.3969/j.issn.1001-5485.2013.12.010

岩土工程

基于组合赋权法和聚类分析法的岩爆预测

  • 郭庆清 ,
  • 刘磊磊 ,
  • 张绍和 ,
  • 王晓密
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  • 1.中南大学a.有色金属成矿预测教育部重点实验室；b.地球科学与信息物理学院,长沙410083；
    2.中国人民武装警察部队水电第七支队,江西鹰潭335000
郭庆清(1967-),男,江西抚州人,高级工程师,博士研究生,主要从事水电基础工程、岩土工程方面的设计与施工,(电话)13917158968(电子信箱)wjsdgqq@163.com。

收稿日期: 2012-11-28

  修回日期: 2013-12-10

  网络出版日期: 2013-12-10

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Prediction of Rockburst by Combination Weight Methodand Cluster Analysis Method

  • GUO Qing-qing ,
  • LIU Lei-lei ,
  • ZHANG Shao-he ,
  • WANG Xiao-mi
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  • 1.Key Laboratory of Metallogenic Prediction of Nonferrous Metals of Ministry of Education,Central South University,Changsha 410083,China;
    2.School of Geosciences and Info-physics,Central South University,Changsha 410083,China;
    3.The Seventh Detachment of Chinese People’s Armed Police Hydropower Troops,Yingtan 335000,China

Received date: 2012-11-28

  Revised date: 2013-12-10

  Online published: 2013-12-10

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摘要

统计国内外部分岩爆数据并作为已知样本,以目前应用较多的影响岩爆预测与评价的3个主要因素为研究指标,即洞室最大切向应力与岩石单轴抗压强度比、岩石单轴抗压强度与岩石单轴抗拉强度比和弹性能量指数,建立岩爆预测的聚类分析模型。根据各因素重要性的不同,采用组合赋权的方法对3个指标赋以一定的权重,使得岩爆数据更加科学合理。采用系统聚类分析法对各岩爆样本数据进行处理和分析,并对5处岩爆实例进行烈度等级预测。结果表明,采用该方法能较好地对岩爆进行分类,并且能够比较准确地预测岩爆发生情况,为岩爆预测提供了另一种依据。

关键词： 岩爆; 预测; 聚类分析; 组合赋权法

本文引用格式

郭庆清 , 刘磊磊 , 张绍和 , 王晓密 . 基于组合赋权法和聚类分析法的岩爆预测[J]. 长江科学院院报, 2013 , 30(12) : 54 -59 . DOI: 10.3969/j.issn.1001-5485.2013.12.010

Abstract

A model of rockburst prediction was established based on cluster analysis method. Some of the rockburst data in China and abroad were collected by statistics and were chosen as known samples. Three major factors which affect the prediction and evaluation of rockburst were selected as indexes. These factors are ratio of cavern’s maximum tangential stress to rock’s uniaxial compressive strength,ratio of rock’s uniaxial compressive strength to uniaxial tensile strength,and elastic energy index. According to the importance of these factors,combination weight method was adopted to give weight to the three factors so as to make the data more scientific and reasonable. The rockburst data were processed and analyzed by using cluster analysis method,and then by using this method,the intensity levels of 5 rockburst examples were predicted. Research findings show that methods in this research could well classify rockburst grades and predict rockburst accurately. It provides a basis for rockburst prediction.

Key words： rockburst; prediction; cluster analysis; combination weight method

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