Cong, Y.; Motohashi, T.; Nakao, K.; Inazumi, S. Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection. Mach. Learn. Knowl. Extr.2024, 6, 402-419.
Cong, Y.; Motohashi, T.; Nakao, K.; Inazumi, S. Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection. Mach. Learn. Knowl. Extr. 2024, 6, 402-419.
Cong, Y.; Motohashi, T.; Nakao, K.; Inazumi, S. Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection. Mach. Learn. Knowl. Extr.2024, 6, 402-419.
Cong, Y.; Motohashi, T.; Nakao, K.; Inazumi, S. Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection. Mach. Learn. Knowl. Extr. 2024, 6, 402-419.
Abstract
The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to make predictions without conducting physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all variables as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.
Copyright:
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