Integrating Machine Learning and Image-Based Damage Quantification to Predict Self-Healing Performance of Asphalt Mixtures Merve Ozkan, Mert Atakan, Kürşat Yildiz IEEE Access, 2026 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.
Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data Mert Atakan, Kürşat Yıldız Road Materials and Pavement Design, 2024 Previous studies have achieved accurate predictions for Marshall design parameters (MDPs), but their limited data and input variables might restrict generalization. In this study, machine learning (ML) was used to predict MDPs with more generalised models. To achieve this, a dataset was collected from six different papers. Inputs were material properties and their ratios in the mixture, while target features were six MDPs used in mixture design. Four ML algorithms were used including linear regression, polynomial regression, k nearest neighbour (KNN) and support vector regression (SVR). Also, the cross-validation (CV) method was used to detect the generalisation capability of the models. Accuracy of the SVR was the highest, however, in nested CV its performance was highly reduced. Therefore, KNN was recommended due to its second highest performance. The results demonstrated that prediction of MDPs from only material properties is possible and promising to use in mixture design. Abbreviations: ANN: artificial neural network; BC: bitumen content; BP: bitumen penetration (1/10 mm); CV: cross-validation; DEM: discrete element method; GA: genetic algorithm; Gmb: Bulk specific gravity of mixture; Gmm: Maximum specific gravity of mixture; Gsb: bulk specific gravity of aggregate; KNN: k nearest neighbour; LA: Los Angeles abrasion; LR: linear regression; MARS: multivariate adaptive regression spline; MDP: Marshall design parameter; MF: Marshall flow; MQ: Marshall quotient (kN/mm); MS: Marshall stability; NMAS: nominal maximum aggregate size; NoB: number of blows; PI: penetration index; PR: polynomial regression; R2: coefficient of determination; SP: softening point (°C); SVR: support vector regression; UPVT: ultrasonic pulse velocity–time; Va: air voids percentage; VFA: voids filled with asphalt; VMA: voids in mineral aggregate; WA: water absorption.
Integrating Machine Learning and Image-Based Damage Quantification to Predict Self-Healing Performance of Asphalt Mixtures M Ozkan, M Atakan, K Yildiz IEEE Access 14, 26742-26766 , 2026 2026
Effect of number and surface area of the aggregates on machine learning prediction performance of recycled hot-mix asphalt M Atakan, J Valentin, K Yıldız Construction and Building Materials 445, 137788 , 2024 2024 Citations: 6
Effect of aggregate damage on self-healing characteristics of asphalt concrete: An image processing-based method M Atakan, K Yıldız Construction and Building Materials 425, 135924 , 2024 2024 Citations: 6
Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data M Atakan, K Yıldız Road Materials and Pavement Design 25 (3), 454-473 , 2024 2024 Citations: 15
Self-healing potential of porous asphalt concrete containing different aggregates and metal wastes through microwave heating M ATAKAN, K YILDIZ Politeknik Dergisi 25 (2), 623 - 631 , 2022 2022 Citations: 9
Modification of Asphalt Binder with Waste Expanded Polystyrene (EPS) Foam K YILDIZ, H KINACI, M Atakan Celal Bayar University Journal of Science 17 (3), 245-252 , 2021 2021 Citations: 11
Improving microwave healing characteristic of asphalt concrete by using fly ash as a filler K Yıldız, M Atakan Construction and Building Materials 262, 120448 , 2020 2020 Citations: 55
A Comparative Life Cycle Assessment of Asphalt and Rigid Pavements M Atakan, AB Gültekin, K Yıldız 6th international Project and Construction Management Conference (e-IPCMC2020) , 2020 2020 Citations: 1
Uçucu Kül İçeren Asfalt Betonunun Kendini İyileştirme Performansının Değerlendirilmesi M Atakan Gazi Üniversitesi Fen Bilimleri Enstitüsü , 2019 2019
Improving Microwave Heating Characteristic of Asphalt Binder by Using Fly Ash M Atakan, K Yıldız 4th International Sustainable Buildings Symposium, 285-293 , 2019 2019 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Improving microwave healing characteristic of asphalt concrete by using fly ash as a filler K Yıldız, M Atakan Construction and Building Materials 262, 120448 , 2020 2020 Citations: 55
Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data M Atakan, K Yıldız Road Materials and Pavement Design 25 (3), 454-473 , 2024 2024 Citations: 15
Modification of Asphalt Binder with Waste Expanded Polystyrene (EPS) Foam K YILDIZ, H KINACI, M Atakan Celal Bayar University Journal of Science 17 (3), 245-252 , 2021 2021 Citations: 11
Self-healing potential of porous asphalt concrete containing different aggregates and metal wastes through microwave heating M ATAKAN, K YILDIZ Politeknik Dergisi 25 (2), 623 - 631 , 2022 2022 Citations: 9
Effect of number and surface area of the aggregates on machine learning prediction performance of recycled hot-mix asphalt M Atakan, J Valentin, K Yıldız Construction and Building Materials 445, 137788 , 2024 2024 Citations: 6
Effect of aggregate damage on self-healing characteristics of asphalt concrete: An image processing-based method M Atakan, K Yıldız Construction and Building Materials 425, 135924 , 2024 2024 Citations: 6
Improving Microwave Heating Characteristic of Asphalt Binder by Using Fly Ash M Atakan, K Yıldız 4th International Sustainable Buildings Symposium, 285-293 , 2019 2019 Citations: 6
A Comparative Life Cycle Assessment of Asphalt and Rigid Pavements M Atakan, AB Gültekin, K Yıldız 6th international Project and Construction Management Conference (e-IPCMC2020) , 2020 2020 Citations: 1
Integrating Machine Learning and Image-Based Damage Quantification to Predict Self-Healing Performance of Asphalt Mixtures M Ozkan, M Atakan, K Yildiz IEEE Access 14, 26742-26766 , 2026 2026
Uçucu Kül İçeren Asfalt Betonunun Kendini İyileştirme Performansının Değerlendirilmesi M Atakan Gazi Üniversitesi Fen Bilimleri Enstitüsü , 2019 2019
