岩体结构面非典型数据集的优势组提取方法

doi:10.13722/j.cnki.jrme.2015.1177

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摘要

岩体结构面产状的随机性导致现场实际测得岩体结构面数据往往包含大量不属于优势组的组间数据，造成数据集的非典型和优势组提取的困难，故提出一种基于截断法的模糊C均值聚类优势组提取方法，该方法具有组间数据剔除机制。人工算例表明，提出的方法在非典型数据集的优势组提取中较传统的FCM法具有明显优势，不仅剔除了组间数据，而且能够保证参与样本处于较高的比例，优势组产状的计算精度更高。同时，比较不同距离函数和聚类中心算法在聚类中的表现，选取适合结构面非典型数据集聚类的参数。将该方法和优化参数应用到某金矿围岩结构面分类中，成功的剔除了数组间数据，实现了在岩体结构面非典型数据集中的优势组提取。

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	刘恺
	李启月
	李夕兵
	王泽伟

关键词 ：岩石力学, 岩体结构面, 非典型数据集, 模糊C均值聚类, 距离函数, 聚类中心算法, 截断法

Abstract：

The occurrence randomness of rock joints leads to generating large amounts of datum that do not belong to any dominant group in the practical datasets. This consequently causes the atypia of the data sets and the difficulty in determining dominant occurrences. In this study，A FCM-based dominant group determining method is developed，and a cut-back method is adopted to eliminate the influence of the data out of the dominant groups. Synthetic tests show that the suggested method is superior to the traditional FCM method for extracting dominant occurrences from atypical data sets. This method can not only eliminate unexpected data but also be able to obtain a high proportion of valid data and accurate dominant occurrences. Then optimal parameters for atypical data sets were obtained by comparing the performances of different distance functions with the cluster center expressions. A practical application of this method to a gold mine shows that the suggested method and the optimized parameters perform well in determining dominant rock joint groups from atypical data sets.

Key words： rock mechanics rock joints atypical data sets fuzzy C-mean clustering distance function clustering center algorithm cut-back method

引用本文:

刘恺，李启月，李夕兵，王泽伟. 岩体结构面非典型数据集的优势组提取方法[J]. 岩石力学与工程学报, 2017, 36(S2): 4018-4024.
LIU Kai，LI Qiyue，LI Xibing，WANG Zewei. A dominant group determination method for atypical data sets of rock joint occurrences. , 2017, 36(S2): 4018-4024.

链接本文:

https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2015.1177 或 https://rockmech.whrsm.ac.cn/CN/Y2017/V36/IS2/4018

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