Machine Learning-Driven Resilient Modulus Prediction for Flexible Pavements Across Mountainous and Other Regions

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Accurate estimation of the elastic modulus (Mr) in the com- pacted subgrade soil is essential for the design of flexible pavement systems that are both reliable and environmentally friendly. Mr significantly affects the structural integrity of the pavement, especially in hilly areas with varying loads and climatic conditions. This study collects 2813 data points from pre- vious research results to create an accurate prediction model. The gradient boosted (GB) machine learning (ML) approach is selected to predict the Mr of the compacted subgrade soil. The accuracy and predictive performance of the GB model were evaluated using statistical analysis that includes fun- damental metrics such as root mean square error, mean absolute error, and relative squared error. The model obtained R² values of 0.96 and 0.94 for the training and testing datasets. The RMSE was 5 MPa for training and 7.48 MPa for testing, while the MAE was 3.18 MPa and 5.55 MPa. These results highlight the potential of GB in predicting soil Mr, thereby supporting the development of more accurate and efficient Mr prediction, ultimately reduc- ing time and cost.