Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane

LI Ning; WANG Li-guan; JIA Ming-tao; CHEN Jian-hong; TAN Zheng-hua

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane

更新时间：2026-06-25

- Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane
- Vol. 35, Issue S2, (2014)
- 作者机构：
  
  1. 中南大学资源与安全工程学院,长沙,410083
  2. 中南大学数字矿山研究中心,长沙,410083
  3. 湘潭大学信息工程学院,湖南,湘潭,411105
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU452
- Online First：12 November 2014，
  
  Published：2014
- 稿件说明：
移动端阅览
LI Ning, WANG Li-guan, JIA Ming-tao, et al. Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane[J]. 2014, 35(S2).
DOI：

LI Ning, WANG Li-guan, JIA Ming-tao, et al. Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane[J]. 2014, 35(S2). DOI：

摘要

岩体结构面控制着岩质边坡和地下洞室等岩体工程的稳定性，在岩体力学及水力学分析中起到关键作用。为对岩体结构面进行合理分组，精确地模拟岩体结构面网络的分布，提出一种融合改进遗传算法和支持向量机的聚类方法。首先，根据岩体结构面产状信息建立结构面分组的数学模型，采用改进的遗传算法计算结构面样本的全局最优聚类中心，再以聚类中心为训练样本，利用支持向量机方法将结构面样本进行完全划分。通过随机产生的结构面数据以及实际工程的运用表明，遗传-支持向量机聚类算法对岩体结构面的分组合理，获得的优势结构面结果可靠。

Abstract

When underground or surface excavations are made in rock masses

the behavior of the surrounding rock material will be greatly influenced by the presence of discontinuities. Various modes of rock slope and wedge failure can be attributed to the existence of fractures in a rock mass. In order to make rock mass structure plane group reasonably

and simulate the distribution of rock mass structural plane network by computer

this paper proposes a fusion of clustering method with improved genetic algorithm and support vector machine. First of all

according to the rock mass structural plane orientation information to establish the mathematical model of structural plane grouping

the global optimal clustering center of structural plane is obtained by using the improved genetic algorithm. Then the structural plane is grouped completely with support vector machine. The application of practical engineering shows that the genetic-support vector machine clustering algorithm for grouping of the rock mass structure plane is reasonable and reliable.

关键词

Keywords

references

Views

1759

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Slope reliability analysis using support vector machine

Slope reliability analysis based on improved genetic algorithm

Application of improved genetic algorithm to optimization of grouting curtain of dam foundatio

Dynamic cluster analysis of discontinuity orientations of jointed rock mass

Progressive fracture evolution and mechanical response of cavity-containing sandstone under low-frequency disturbance

Related Author

HE Ting-ting

SHANG Yue-quan

Lü Qing

REN Shan-shan

XIE Gui-hua

ZHANG Jia-sheng

LI Ji-xiang

CUI Wen-juan

Related Institution

College of Civil Engineering and Architecture, Zhejiang University

Key Laboratory of Soft Soils and Geoenvironmental Engineering of Ministry of Education, Zhejiang University

School of Civil Engineering and Architecture, Central South University

Faculty of Science, Jiangsu University

Department of Civil Engineering, Wuhan Polytechnic University

AI问答

⁰