基于主成分分析法的Q2黄土湿陷特性研究
A study of the collapsibility of Q2 loess based on principal component analysis
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摘要: Q2黄土由于埋藏深，结构相对致密，其湿陷性问题常常被忽视。湿陷系数作为评价黄土湿陷程度的定量指标，其影响因素众多，包括土的含水率、干密度、孔隙比等。由于各因素之间存在一定相关性，所建立的湿陷系数与物理指标之间相关关系往往准确度较低。为降低黄土湿陷指标多重相关性对数据回归分析结果的影响，提高预测精度，以彬州渭化乙二醇项目场地Q2黄土为研究对象，在统计分析场地地层物性指标及湿陷系数与物性单一指标之间相关性的基础上，筛选了7个与湿陷系数相关性较好的指标。采用主成分分析法，通过多元线性回归分析，建立了以累积方差贡献率为基础的Q2黄土湿陷系数计算模型。模型计算值与实测值对比结果表明，该方法有效较低了湿陷系数影响因子之间的多重相关性和相互影响问题，证实了所建立的Q2黄土湿陷系数与独立影响因子之间相关关系的合理性和准确性。Abstract: Due to deep burial and the relatively dense structure of Q2 loess, its collapsibility is often overlooked. As a quantitative index to evaluate the degree of loess collapsibility, collapsibility coefficient is influenced by many factors, including soil moisture content, dry density, void ratio and other physical properties. Because there is a certain correlation among the factors, the correlation between the established collapsibility coefficient and the physical index is often of low accuracy. In order to effectively reduce the influence of multiple correlation of loess collapsible index on the data regression analysis results and to improve the prediction accuracy, the Q2 loess of the Weihua glycol project site in Binzhouis taken as the research object. Based on the statistical analysis of the correlation between the physical property index of the site stratum and the single index of collapsible coefficient and physical property, seven indexes with better correlation with collapsible coefficient are selected. By using principal component analysis and multiple linear regression analysis, the calculation model of Q2 loess collapsibility coefficient based on cumulative variance contribution rate is established. The comparison between the calculated value of the model and the measured value shows that the method is effective in reducing the multiple correlation and mutual influence between the influence factors of the collapsibility coefficient, and confirms the rationality and accuracy of the correlation between the established Q2 loess collapsibility coefficient and the independent influence factors.
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Keywords:
- principal component analysis /
- Q2 loess /
- collapsibility /
- physical indices /
- correlation analysis
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