Akademik Veri Yönetim Sistemi
IEEE Access, cilt.14, ss.26742-26766, 2026 (SCI-Expanded, Scopus)
This study presents a machine-learning framework that predicts a fracture-based healing index of asphalt mixtures by explicitly incorporating image-quantified fracture-surface damage modes (adhesive, cohesive, aggregate). Damage types were quantified through digital image processing. Two datasets were employed: one with specimens broken at 20 °C and another with variable temperatures ( 20 °C to 20 °C). Eight feature sets were developed to isolate key factors, and multiple ML models were tested. Results showed that breaking temperature is the most dominant factor influencing healing, though its strong correlation can create spurious relationships that mask the effects of mixture properties. When temperature was fixed, aggregate damage consistently emerged as the most reliable predictor, with the best performance achieved by Support Vector Regressor (R2 = 0.856 at 20 °C). Bitumen content showed gradation-dependent effects: in porous mixtures, higher binder reduced aggregate damage, while in dense mixtures the effect was negligible. Regardless of gradation, higher binder content enhanced healing by improving crack filling and binder flow. Air voids also showed contrasting effects: healing decreased with higher voids in dense mixtures, but moderate voids in porous mixtures facilitated binder redistribution and improved healing. Among the algorithms, Support Vector Regressor achieved the highest predictive accuracy, followed by Gradient Boosting, while Linear Regression underperformed, reflecting the nonlinear nature of healing. Feature selection with Recursive Feature Elimination and Cross-Validation (RFECV) improved efficiency with minor accuracy loss, though excluding aggregate damage reduced reliability. Sensitivity analyses confirmed that breaking temperature dominated predictions at variable conditions, while at fixed temperature, volumetric properties and cohesive damage became more influential. These findings demonstrate the potential of ML to capture complex healing mechanisms and support mix design strategies tailored to gradation type and service temperature.