砂土液化等级的多指标综合预测模型可以分为2种类型:第1类以砂土液化等级分类标准为基础,第2类以实例数据为基础。分别选择砂土液化分类标准生成的数据以及实例数据作为样本,采用主成分分析法对样本数据进行降维处理,以Logistic回归模型来描述砂土液化影响因素与砂土液化等级之间的对应关系,建立了2种类型的主成分-Logistic回归模型。实例应用结果显示,2种类型的主成分-Logistic回归模型都具有一定的可行性,第2种类型的主成分-Logistic回归模型预测结果更符合实际情况,在具有较多实例数据时,更具有应用价值。
Abstract
The multi-index models of predicting soil liquefaction level can be divided into two types: models based on classification standard of soil liquefaction level, and models based on instance data. In this research, instance data and data produced by stochastic interpolation based on classification standard were used as training samples. Dimension reduction of the samples was conducted through principal component analysis (PCA), and logistic regression model was adopted to describe the relationship between soil liquefaction level and its influencing factors. Hence the PCA-Logistic models were established for the two model types. Case study proves that the PCA-Logistic models are feasible in the prediction of soil liquefaction level. But the prediction result of the second type (which is based on instance data) of PCA-Logistic model is more in line with the actual situation, and especially has more practical value in the presence of more instance data.
关键词
砂土液化等级 /
影响因素 /
预测 /
主成分 /
Logistic回归模型
Key words
soil liquefaction level /
impact factor /
prediction /
principal component analysis(PCA) /
Logistic regression model
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