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地下工程岩爆预测的综合集成方法
引用本文:李天斌,肖学沛.地下工程岩爆预测的综合集成方法[J].地球科学进展,2008,23(5):533-540.
作者姓名:李天斌  肖学沛
作者单位:成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都,610059;成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都,610059;四川省交通厅公路规划勘察设计研究院,四川成都,610041
基金项目:国家自然科学基金 , 教育部科学技术研究项目
摘    要:鉴于地下工程中岩爆预测的各种方法的局限性以及预报准确率低的现状,提出了地下工程岩爆综合集成预测的学术思路。这种学术思路将定性预测与定量预测相结合、单因素预测与多因素预测相结合,引入处理复杂性问题行之有效的非线性科学理论,对岩爆进行综合集成预测。详细介绍了岩爆预测的综合集成方法,包括:地质分析预测法、应力强度比法、层次分析—模糊评判法和神经网络法,并将其应用于雅砻江某水电站交通隧道岩爆预测中,取得了较好的效果。

关 键 词:岩爆预测  应力强度比法  层次分析——模糊评判法  神经网络法
文章编号:1001-8166(2008)05-0533-08
收稿时间:2008-04-12
修稿时间:2008年4月12日

Comprehensively Integrated Methods of Rockburst Prediction in Underground Engineering
LI Tianbin,XIAO Xuepei.Comprehensively Integrated Methods of Rockburst Prediction in Underground Engineering[J].Advance in Earth Sciences,2008,23(5):533-540.
Authors:LI Tianbin  XIAO Xuepei
Institution:1.State Key Labortory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059,China; 2.Sichuan Province Communication Department Highway Planning, Survey, Design and Research Institute, Chengdu 610041,China
Abstract:In view of the low accuracy of rockburst prediction in underground engineering and the flaws of existing methods of prediction, an academic thought of comprehensively integrated prediction for rock burst is put forward in the paper. According to the thought, the rockburst prediction must combine the qualitative prediction with the quantitative forecast, the single factor prediction with the many factors forecast. Nonlinear scientific theory, which is effective in solution of complex problems, should be used to the comprehensively integrated prediction for rock burst. In this paper, the comprehensively integrated methods of rockburst prediction are recommended in detail, including comprehensive geologic analysis, stress strength ratio method, AHP FUZZY assessment and neural network method. These methods were used to forecast the rockburst of traffic tunnels in Jinping Ⅱ Hydropower Station on Yalong river in west China, obtaining a good results.
Keywords:Rockburst prediction  Stress strength ratio method  AHP FUZZY assessment method  Neural network method
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