Prediction of Soil Liquefaction Level Based on Principal Component Analysis and Logistic Regression Model

WANG Jun-long

doi:10.11988/ckyyb.20140348

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Journal of Changjiang River Scientific Research Institute ›› 2015, Vol. 32 ›› Issue (9) : 134-139. DOI: 10.11988/ckyyb.20140348

ROCKSOIL ENGINEERING

Prediction of Soil Liquefaction Level Based on Principal Component Analysis and Logistic Regression Model

WANG Jun-long

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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.

Key words

soil liquefaction level / impact factor / prediction / principal component analysis(PCA) / Logistic regression model

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WANG Jun-long. Prediction of Soil Liquefaction Level Based on Principal Component Analysis and Logistic Regression Model[J]. Journal of Changjiang River Scientific Research Institute. 2015, 32(9): 134-139 https://doi.org/10.11988/ckyyb.20140348

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