Assessment of resilient modulus of soil using hybrid extreme gradient boosting models

Accurate estimation of the soil resilient modulus (M_R) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M_R efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed. In BKA-XGBOOST, XGBOOST captures the many-to-one nonlinear relationship between geotechnical factors and M_R, while BKA provides the optimal hyperparameters for XGBOOST. By combining them, XGBOOST has stable and accurate predictive capabilities for different combinations of soil data. Comparisons with nine models show that the proposed model outperforms other models in terms of M_R prediction accuracy, with a determination coefficient (R²) of 0.995 and a mean absolute error (MAE) of 0.975 MPa. In addition, an efficient M_R prediction software is developed based on the model to improve its practicality and interactivity, which is promising for assisting engineers in evaluating pavement properties.