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节理岩体结构面产状的动态聚类分析
引用本文:范雷,王亮清,唐辉明.节理岩体结构面产状的动态聚类分析[J].岩土力学,2007,28(11):2405-2408.
作者姓名:范雷  王亮清  唐辉明
作者单位:1.中国地质大学 工程学院,武汉 430074;2. 武汉理工大学 土木工程与建筑学院,武汉 430070
摘    要:节理岩体结构面的优势产状是进行Mnote-carlo模拟和岩体稳定性分析的基础。玫瑰花图、等密度图等传统的图形分析方法比较粗糙,其结果只是相对的优势组数划分,无法准确地给出结构面的优势产状,使得分组结果在实际中应用不便。为弥补上述不足,采用改进的动态聚类分析方法,构造空间直角坐标系,以结构面的单位法向量表示其产状,并根据球面上两点之间的球面距离对结构面产状进行分类判定。将该方法应用于三峡库区巴东新县城铜盆溪桥东头高切坡结构面优势产状分析中。结果表明,采用改进动态聚类分析方法结果可靠,分类合理,可以较准确地确定结构面的优势产状。

关 键 词:节理岩体  结构面  优势产状  动态聚类  球面距离  
文章编号:1000-7598-(2007)11-2405-04
收稿时间:2005-11-01
修稿时间:2005-11-01

Dynamic cluster analysis of discontinuity orientations of jointed rock mass
FAN Lei,WANG Liang-qing,TANG Hui-ming.Dynamic cluster analysis of discontinuity orientations of jointed rock mass[J].Rock and Soil Mechanics,2007,28(11):2405-2408.
Authors:FAN Lei  WANG Liang-qing  TANG Hui-ming
Institution:1. Engineering Faculty, China University of Geosciences, Wuhan 430074, China; 2. School of Civil and Architectural Engineering, Wuhan University of Technology, Wuhan 430070, China
Abstract:The analysis of dominant orientations of discontinuities is a basic work for a further study on simulating with Monte-Carlo method and conforming the stability of rock mass.Traditional analysis of plots method is insufficient and inadequate.The result is only a simply classification of the orientations,and is inconvenient to be applied in practice.Because of the deficiency of traditional analysis method,a dynamic clustering algorithm is developed.According to the rule of right hand,a three-dimensional field coordinate system is constructed.A unit normal can be used to represent the orientation of discontinuity.Then the orientations are classified by the dots' spherical distance.A data investigated from a slope in the new town of Badong County in the Three Gorges Reservoir is used to be a case study.The study shows that the results of dynamic cluster algorithm are reliable and reasonable.And the dominant orientations and classification are more precision.
Keywords:jointed rock mass  discontinuities  dominant orientation  dynamic cluster algorithm  spherical distance
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