长江科学院院报 ›› 2016, Vol. 33 ›› Issue (8): 96-99.DOI: 10.11988/ckyyb.20150519

• 岩土工程 • 上一篇    下一篇

一种改进的岩体结构面产状快速聚类分析法

李俊,胡斌,祝凯,寇天,王炜   

  1. 中国地质大学武汉 工程学院,武汉 430074
  • 收稿日期:2015-06-23 修回日期:2015-07-13 出版日期:2016-07-25 发布日期:2016-07-25
  • 作者简介:李俊(1991-),男,湖北武汉人,硕士研究生,主要从事边坡稳定性分析、数值模拟研究,(电话)13971308429(电子信箱)1004536586@qq.com。
  • 基金资助:
    国家自然科学基金项目(41172281);国家科技部973项目(2011CB710604);中央高校基本科研业务费专项资金项目(摇篮人才计划CUGL100413,特色学科团队CUG090104)

An Improved Fast Clustering Analysis Method for Attitude of Structural Plane in Rock Mass

LI Jun,HU Bin,ZHU Kai,KOU Tian,WANG Wei   

  1. Faculty of Engineering,China University of Geosciences,Wuhan 430074,China
  • Received:2015-06-23 Revised:2015-07-13 Published:2016-07-25 Online:2016-07-25

摘要: 岩体结构面的优势产状是进行岩体工程研究与分析的基础,传统的玫瑰花图、极点等密度图等方法精度差,其结果往往只是给出优势组数。为了准确地给出结构面的优势产状,本文采用改进的快速聚类分析方法,将结构面产状表示为空间单位上半球体球面上的点,通过构造密度函数求取各数据点加权值对结构面产状进行分类判定。同时,将该方法应用于某矿山运输公路边坡优势结构面的统计分析之中。结果表明采用该方法分析结果可靠,分类合理,能准确得出优势结构面产状,能有效消除孤立点对聚类结果的不利影响。

关键词: 岩体结构面, 优势产状, 快速聚类分析, 密度函数, 统计分析

Abstract: Dominant attitudes of structural plane in rock mass are fundamental to the research and analysis of geotechnical engineering. Conventional rose diagram and pole isodense diagram are of poor accuracy, just with results of the number of dominant groups. In order to accurately obtain the dominant attitudes of structural plane, we proposed an improved fast clustering analysis method to turn structural plane attitudes into points on hemisphere surface of the unit space. According to structure density functions, we calculated the weighted values of every data point and classified the attitudes of structural planes. Furthermore, we applied the method to the statistical analysis of dominant structural plane of a slope near a mine transportation road. Results show that this method is reliable and reasonable. We can use it to determine the dominant attitudes of structural plane, and effectively avoid adverse effect of isolated points on the clustering result.

Key words: structural plane of rock mass, dominant attitude, fast clustering analysis, density function, statistical analysis

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