Machine learning‐based analysis of interaction effects among influencing factors on the resilient modulus of stabilized aggregate base

To overcome the limitations of conventional single-factor analysis, this study proposed a framework for investigating interaction effects of influencing factors on the resilient modulus (Mr) of stabilized aggregate base. First, cross-validation was utilized to compare the predictive accuracy and generalization capability of gradient boosting (GB) and random forest (RF) in predicting the Mr. The grid search algorithm was used to optimize hyperparameters. After optimization, the coefficient of determination for GB reached 0.99 on the training set and 0.96 on the test set, while those for RF were 0.98 and 0.94, respectively. The results indicated that GB demonstrated higher predictive accuracy for the Mr. Finally, the importance analysis, univariate sensitivity analysis, and bivariate interaction sensitivity analysis of influencing factors were systematically conducted using partial dependence plots (PDP) and Shapley additive explanations (SHAP). The research results showed that the importance of influencing factors on the Mr decreases in the order of maximum dry density to optimum moisture content ratio, wet–dry cycles (WDC), deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials. The bivariate interaction sensitivity analysis of the WDC, deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials did not disrupt their single-variable sensitivity relationships with the Mr. The variation of the WDC would destroy the single variable sensitivity relationship between the optimum moisture content ratio and Mr.