Machine Learning Prediction of Road Performance of Cold Recycled Mix Asphalt with Genetic Algorithm Hyperparameter Optimization.

With the rapid expansion and aging of global road networks, cold recycled mix asphalt (CRMA) has gained significant attention as a sustainable pavement rehabilitation technology. However, the road performance of CRMA is highly sensitive to material composition and curing conditions, making accurate performance prediction challenging. This study develops machine learning (ML) models to predict two critical performance indicators: dynamic stability (DS) for high-temperature stability and indirect tensile strength (ITS) for low-temperature crack resistance. Four ML algorithms, Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), were trained on a comprehensive dataset of 436 samples. A genetic algorithm (GA) was employed to optimize model hyperparameters, significantly enhancing prediction accuracy and robustness. The SHAP method was further applied to interpret model outputs and identify key influencing factors. Results demonstrate that GA-optimized XGBoost achieved the highest predictive performance for both dynamic stability (DS) and indirect tensile strength (ITS), with R