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

raybet体育在线 院报 ›› 2013, Vol. 30 ›› Issue (12) : 54-59.

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raybet体育在线 院报 ›› 2013, Vol. 30 ›› Issue (12) : 54-59. DOI: 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1a,1b,2,刘磊磊1a,1b,张绍和1a,1b,王晓密1b
作者信息 +

Prediction of Rockburst by Combination Weight Methodand Cluster Analysis Method

  • GUO Qing-qing1,2,3,LIU Lei-lei1,2,ZHANG Shao-he1,2,WANG Xiao-mi2
Author information +
文章历史 +

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。

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.

关键词

岩爆 / 预测 / 聚类分析 / 组合赋权法

Key words

rockburst / prediction / cluster analysis / combination weight method

引用本文

导出引用
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]. raybet体育在线 院报. 2013, 30(12): 54-59 https://doi.org/10.3969/j.issn.1001-5485.2013.12.010
GUO Qing-qing,LIU Lei-lei,ZHANG Shao-he,WANG Xiao-mi. Prediction of Rockburst by Combination Weight Methodand Cluster Analysis Method[J]. Journal of Changjiang River Scientific Research Institute. 2013, 30(12): 54-59 https://doi.org/10.3969/j.issn.1001-5485.2013.12.010
中图分类号: U452   

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