| 改进遗传算法和支持向量机的岩体结构面聚类分析 |
| |
| 引用本文： | 李宁,王李管,贾明涛,陈建宏,谭正华.改进遗传算法和支持向量机的岩体结构面聚类分析[J].岩土力学,2014,35(Z2):405-411. |
| |
| 作者姓名： | 李宁 王李管 贾明涛 陈建宏 谭正华 |
| |
| 作者单位： | 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 |
| |
| 基金项目： | 中国高技术研究发展计划(“863”计划)项目(No. 2011AA060407)；国家自然科学基金(No. 51374242)；湖南省自然科学基金(No. 14JJ3077)；国家留学基金委(201306370143)。 |
| |
| 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。
|
| 关 键 词： | 结构面聚类分析 改进遗传算法 支持向量机（SVM） 优势产状 |
| 收稿时间： | 2012-11-30 |
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.Application of improved genetic algorithm and support vector machine to clustering analysis of rock mass structural plane[J].Rock and Soil Mechanics,2014,35(Z2):405-411. |
| |
| Authors: | LI Ning WANG Li-guan JIA Ming-tao CHEN Jian-hong TAN Zheng-hua |
| |
| Institution: | 1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China; 2. Digital Mine Research Center, Central South University, Changsha 410083, China; 3. College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105, China |
| |
| 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. |
| |
| Keywords: | clustering analysis of structural plane improved genetic algorithm support vector machine (SVM) dominant orientation |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《岩土力学》浏览原始摘要信息 |
| 点击此处可从《岩土力学》下载免费的PDF全文 |
