| 基于改进遗传算法的岩体结构面产状聚类分析 |
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| 引用本文： | 崔学杰,晏鄂川,陈武.基于改进遗传算法的岩体结构面产状聚类分析[J].岩土力学,2019,40(Z1):374-380. |
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| 作者姓名： | 崔学杰 晏鄂川 陈武 |
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| 作者单位： | 中国地质大学(武汉) 工程学院，湖北 武汉 430074 |
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| 基金项目： | 国家自然科学基金项目（No.41172282，No.41672313） |
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| 摘 要： | 根据产状对结构面进行分组是研究岩体结构的重要环节。传统分组方法通常需要依靠地质经验，缺乏客观性，而现有的聚类方法也存在一些缺陷。基于变长度字符串遗传算法，提出了一种改进的K均值算法，实现了岩体结构面产状的自动聚类。该方法的核心思想是使用遗传算法为K均值算法选择恰当的聚类中心，克服了K均值(K-means)算法受初始聚类中心影响，易收敛于局部最优解的缺陷。由于使用了变长度字符串，该方法能够在聚类过程中自动确定最佳结构面组数，同时提供最优的分组结果。针对产状数据，提出了一种新的变异方法，该方法利用C++语言实现，并被应用于浙江省某地下水封洞库结构面产状数据的分析，得到较为合理的分组结果，证明了该方法的有效性。
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| 关 键 词： | 岩体结构面 产状数据 K均值算法 变长度字符串遗传算法 |
| 收稿时间： | 2018-10-27 |
Cluster analysis of discontinuity occurrence of rock mass based on improved genetic algorithm |
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| CUI Xue-jie,YAN E-chuan,CHEN Wu.Cluster analysis of discontinuity occurrence of rock mass based on improved genetic algorithm[J].Rock and Soil Mechanics,2019,40(Z1):374-380. |
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| Authors: | CUI Xue-jie YAN E-chuan CHEN Wu |
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| Institution: | Faculty of Engineering, China University of Geosciences, Wuhan，Hubei 430074, China |
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| Abstract: | Grouping structural planes based on their occurrences is an important way in analyzing the structure characteristics of rock mass. Traditional classification methods usu CUI Xue-jie, YAN E-chuan, CHEN Wu
ally greatly rely on geological experience of researchers, which is lack of objectivity. The powerful clustering methods, however, also have drawbacks. In this paper, based on variable length string genetic algorithm, an improved K-means clustering method was proposed, making the automatic clustering of discontinuity occurrence of rock mass available. The essence of the proposed method is to select appropriate initial cluster centers for K-means algorithm applying the genetic algorithm. It overcomes the limitation that the K-means algorithm is usually affected by the initial cluster center and easily converges to the local optimal solution. The application of variable length strings in the improved algorithm of classification, however, can not only automatically determine the number of the optimal structural plane groups during the clustering process, but also provide optimal grouping results. In addition, a new mutation algorithm is proposed based on the occurrence data. It is realized in C++ and is applied in analyzing the occurrence of structural planes in an underground water-sealing rock caverns located at Zhejiang province, China. Reasonable classification results are achieved, proving the applicability of the proposed approach in this paper. |
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| Keywords: | rock mass discontinuity occurrence data K-means algorithm variable length string genetic algorithm |
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