黄土颗粒体微结构的主成分分析方法研究

发布时间：2018-04-16 21:22

本文选题：黄土 + 微结构　；参考：《武汉理工大学学报》2015年10期

【摘要】：针对21个黄土样本,利用SEM图像采集其微结构参数,发现微结构参数众多,量纲与所表达的物理意义各不相同,各参数表示的信息多有重复。针对以上问题,提出了采用主成分分析法研究某域范围内颗粒体微结构参数的方法。结果表明,前3个主成分的综合贡献率为96%,可以反映所有原始微结构参数的大部分信息,不但减少了"信息重复",还降低了颗粒体微结构参数的维数。同时发现,第1主成分的分散性最大,随主成分次序增加,分散性逐渐减小,第9主成分分散性最小。降维之后,主成分指标减少,这些主成分指标可以用来建造本构方程等土力学关系,且可以通过建立的力学关系来研究土体微结构的再造过程。
[Abstract]:According to 21 loess samples, the SEM image is used to collect the microstructure parameters. It is found that there are many microstructural parameters, the dimension and the physical meaning are different, and the information expressed by each parameter is duplicated.In order to solve the above problems, a principal component analysis (PCA) method is proposed to study the microstructural parameters of granular bodies in a certain range.The results show that the total contribution rate of the first three principal components is 96, which can reflect most of the information of all the original microstructural parameters, which not only reduces the "information repetition", but also reduces the dimension of the granular microstructural parameters.It is also found that the dispersion of the first principal component is the largest, with the increase of the order of the principal component, the dispersibility decreases gradually, and that of the ninth principal component is the least.After dimensionality reduction, the principal component indexes can be used to construct soil mechanics relations such as constitutive equations, and the reconstruction process of soil microstructures can be studied by the established mechanical relationships.
【作者单位】：河南理工大学万方科技学院;郑州科技学院土木工程学院;天津城建大学土木工程学院;大连民族大学土木建筑工程学院;
【基金】：中央高校基本科研业务费专项资金资助项目(201502040402)
【分类号】：TU444

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