Data-Driven Optimization of Sustainable Asphalt Overlays Using Machine Learning and Life-Cycle Cost Evaluation

The growing demand for sustainable pavement materials has driven increased interest in asphalt mixtures incorporating recycled crumb rubber (CR). While CR modification enhances mechanical performance and durability, its often increases initial production costs and energy demand. This study develops an integrated framework that combines machine learning (ML) and economic analysis to identify the optimal balance between performance and cost in CR-modified asphalt overlay mixtures. An experimental dataset of conventional and CR-modified mixtures was used to train and validate multiple ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR). The RF and ANN models exhibited superior predictive accuracy (R2 > 0.98) for key performance indicators such as Marshall stability, tensile strength ratio, rutting resistance, and resilient modulus. A Cost–Performance Index (CPI) integrating life-cycle cost analysis was developed to quantify trade-offs between performance and economic efficiency. Environmental life-cycle assessment indicated net greenhouse gas reductions of approximately 96 kg CO2-eq per ton of mixture despite higher production-phase emissions. Optimization results indicated that a CR content of approximately 15% and an asphalt binder content of 4.8–5.0% achieve the best performance–cost balance. The study demonstrates that ML-driven optimization provides a powerful, data-based approach for guiding sustainable pavement design and promoting the circular economy in road construction.