Review on Artificial Intelligence-Based Prediction of Asphalt Binder Aging and Pavement Distress
Keywords:
Asphalt pavements, bleeding prediction, Artificial neural network (ANN), pavement performance, skid resistance, sensitivity analysisAbstract
Asphalt binder aging significantly affects the long-term performance and durability of flexible pavements, leading to various pavement distresses such as cracking, rutting, and raveling. Accurate prediction of asphalt binder aging and associated pavement deterioration is essential for effective pavement design, maintenance planning, and lifecycle cost management. In recent years, Artificial Intelligence (AI) techniques have emerged as powerful tools for modeling complex, nonlinear relationships among environmental factors, material properties, traffic loading, and aging characteristics of asphalt binders. This review paper explores the application of Artificial Intelligence–based methods for predicting asphalt binder aging and pavement distress. Various AI techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Machine Learning (ML), Deep Learning (DL), and hybrid optimization models are examined in terms of their ability to analyze large datasets and provide accurate predictions. The study reviews existing literature on AI-driven predictive models that incorporate factors such as temperature variation, oxidation processes, traffic load, binder composition, and environmental conditions. Furthermore, the paper highlights the advantages of AI models over traditional empirical and mechanistic methods, including improved prediction accuracy, adaptive learning capability, and efficient data-driven decision-making. The review also identifies current research gaps, challenges in data availability, and opportunities for integrating AI with mechanistic–empirical pavement design approaches. Overall, the study provides a comprehensive overview of recent advancements in AI-based prediction of asphalt binder aging and pavement distress, emphasizing its potential to enhance pavement performance evaluation, optimize maintenance strategies, and support sustainable infrastructure development.
References
Ghasemi, F., Yaghoubi, E., Rajabipour, A., van Staden, R., & Ghasemi, A. (2026). Climate-driven machine learning models for predicting asphalt pavement surface temperature for aging assessments. Engineering Applications of Artificial Intelligence, 167, 113897.
Da, Y., Gao, Y., Li, Y., Ren, D., Liu, K., Bras, A., & Shaw, A. (2025). Advances in smart technologies and materials for automated asphalt pavement inspection: Toward transport infrastructure digitalisation. Automation in Construction, 180, 106523.
Hanandeh, S., Aneke, F. I., Bodour, W., & Imam, R. (2025). Measurement and Modelling Effect of Street Trees Distress on Asphalt Pavements by using Experimental and 3-D Finite Element Methods. Transportation Engineering, 100394.
Zhang, R., Hu, P., Zhong, Y., Wen, L., Ji, J., & Liang, L. (2025). Research progress on evaluation and prediction of degradation in service performance for asphalt pavement. journal of traffic and transportation engineering (english edition), 12(4), 1011-1039.
Nasertork, A., Ranjbar, S., Rahai, M., & Nejad, F. M. (2024). Pavement raveling inspection using a new image texture-based feature set and artificial intelligence. Advanced Engineering Informatics, 62, 102665.
Chen, L., Wang, Y., Chen, Q., & Wang, C. (2025). Research hotspots and trend analysis of intelligent asphalt pavement technology based on CiteSpace. journal of traffic and transportation engineering (english edition).
Ranjbar, S., Nejad, F. M., & Zakeri, H. (2024). Image-based severity analysis of asphalt pavement bleeding using a metaheuristic-boosted fuzzy classifier. Automation in Construction, 166, 105655.
Rad, S. M., Sadeghi, M., Bausano, J., Vivanco, D., & Elkashef, M. (2025). Evaluation of asphalt mixture cracking resistance and development of a machine learning-based application for cracking tolerance index prediction. Construction and Building Materials, 490, 142519.
Tabrizi, M. T., Pakenari, M. M., Hamedi, G. H., Asgharzadeh, S. M., & Suleiman, N. (2025). A comprehensive framework for evaluating the impact of rehabilitation strategies on pavement sustainability. Case Studies in Construction Materials, e05483.
Al-Jarazi, R., Rahman, A., Ai, C., Li, C., & Al-Huda, Z. (2024). Development of a novel prediction model for interface shear strength in asphalt pavement using the CART model. KSCE Journal of Civil Engineering, 28(8), 3246-3256.
Zeng, Z., Underwood, B. S., & Kim, Y. R. (2024). A state-of-the-art review of asphalt mixture fracture models to address pavement reflective cracking. Construction and Building Materials, 443, 137674.
Khadijeh, M., Kasbergen, C., Erkens, S., & Varveri, A. (2025). Combining deep neural networks and Gaussian processes for asphalt rheological insights. Results in Engineering, 26, 105629.
Furtado, L. S., Bessa, I. S., Gurjao, N. D. O., & Soares, J. B. (2025). Integrating smart city technologies for sustainable pavement infrastructure. Canadian Journal of Civil Engineering, 52(5), 569-582.
Luo, X., Huang, J., Shen, L. A., Wang, H., & Shi, Z. (2026). Physics-Informed Neural Networks for Constitutive Modeling and Multiphysics Coupling in Viscoelastic Materials: Applications to Asphalt Pavement Mechanics. Neural Networks, 108803.
Wang, X., Chen, H., Xu, A., Huan, X., Qi, C., Han, S., ... & Kuang, D. (2025). Thermal management in cooling pavements: Advancements in harnessing phase change materials to mitigate urban heat island effect. Journal of Energy Storage, 138, 118628.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
