A Ensemble Machine Learning Prediction Model for Sub-consolidation Coefficient
The long-term settlement problem in engineering practice is controlled by the sub-consolidation coefficient,but the existing em-pirical models of sub-consolidation coefficient have many problems such as inadequate consideration of factors and insufficient research ob-jects,which are not reliable enough.Unlike previous correlation studies,this study proposes an ensemble machine learning model for predict-ing secondary consolidation coefficients.The new model uses four integrated learning methods:random forest,adaptive boosting method,gra-dient boosting regression tree,and extreme gradient boosting method,and adopts grid search method and k-fold cross-validation method to optimize the hyperparameters of the model.This study establishes a complex relationship between four input variables(liquid limit,plasticity index,void ratio,clay content)and one output variable(sub-consolidation coefficient).And the relative importance of features is analyzed,which improves the interpretability of the model.The research results found that the prediction effect of the extreme gradient boosting meth-od is the best,while the prediction effect of random forests is the worst.The void ratio is the most important of the four characteristics,while the relative characteristic of clay content is the least important.
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