Journal of Transportation Research

Journal of Transportation Research

Evaluation of Factors Affecting Asphalt Pavement Distress Using a Random Forest Model

Document Type : Original Article

Authors
Mohamad Amin Ebrahimzadeh 1
Ali Abdi Kordani 2
1 Department of Civil Engineering - Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Professor, Department of Civil Engineering - Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
10.22034/tri.2026.565637.3416
Abstract
Asphalt pavement in urban streets plays a key role in improving safety, comfort, and the quality of transportation services. However, its frequent deterioration in densely populated cities—such as Karaj—results in considerable economic and social costs. This study aims to identify the factors influencing asphalt pavement deterioration and to propose solutions for enhancing its durability and performance. Relevant data on pavement age, traffic volume, the proportion of heavy vehicles, climatic conditions, material quality, and maintenance status were collected, and the level of distress was classified into three categories: low, moderate, and high.To analyze the data and predict the distress level, the Random Forest machine learning algorithm was employed. The results indicated that the model achieved an accuracy of approximately 96.7%, and the most influential factors affecting pavement deterioration were, respectively, material quality, the percentage of heavy vehicles, pavement age, and daily traffic volume, while climatic variables such as temperature and precipitation had less impact.

The confusion matrix analysis also showed the model’s strong ability to identify moderate and severe distress levels, with minor misclassification occurring mainly in predicting low distress. Based on the findings, rapid asphalt deterioration is attributed to a combination of traffic-related, construction-related, and maintenance-related factors. Recommended strategies include using higher-quality materials, increasing asphalt layer thickness, managing heavy-vehicle traffic, implementing preventive maintenance, and utilizing modern additives. These measures can extend pavement service life and reduce maintenance costs.
Keywords
Asphalt pavement
pavement surface distress
Random Forest model
data-driven analysis
Subjects
-Aliha, M. R. M., & Sarbijan, M. J. (2016). Effects of loading, geometry and material properties on fracture parameters of a pavement containing top-down and bottom-up cracks. Engineering Fracture Mechanics, 166, 182–197.
-Aliha, M. R. M., Fazaeli, H., Aghajani, S., & Nejad, F. M. (2015). Effect of temperature and air void on mixed mode fracture toughness of modified asphalt mixtures. Construction and Building Materials, 95, 545–555.
-Alsheyab, M. A., Khasawneh, M. A., Abualia, A., & Sawalha, A. (2024). A critical review of fatigue cracking in asphalt concrete pavement: A challenge to pavement durability. Innovative Infrastructure Solutions, 9(10), 386.
-Bhandari, S., Luo, X., & Wang, F. (2023). Understanding the effects of structural factors and traffic loading on flexible pavement performance. International Journal of Transportation Science and Technology, 12(1), 258–272.
-Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
-Chatti, K., Salama, H., & El Mohtar, C. (2004). Effect of heavy trucks with large axle groups on asphalt pavement damage. In Proc., 8th Int. Symp. on Heavy Vehicle Weights and Dimensions.
-Dong, Q., & Huang, B. (2012). Evaluation of effectiveness and cost-effectiveness of asphalt pavement rehabilitations utilizing LTPP data. Journal of Transportation Engineering, 138(6), 681–689.
-Dong, Q., Jiang, X., Huang, B., & Richards, S. H. (2013). Analyzing influence factors of transverse cracking on LTPP resurfaced asphalt pavements through NB and ZINB models. Journal of Transportation Engineering, 139(9), 889–895.
-Ebrahimzadeh, M. A., Abdi Kordani, A., & Nakhaie, N. (2024). Optimizing Overload Reduction in Vehicles Using Moving Weighing System. Journal of Transportation Research, 21(3), 221-238.
-Fathi, A., Mazari, M., Saghafi, M., Hosseini, A., & Kumar, S. (2019). Parametric study of pavement deterioration using machine learning algorithms. In International Airfield and Highway Pavements Conference 2019 (pp. 31–41). ASCE.
-Fazaeli, H., Samin, Y., Pirnoun, A., & Dabiri, A. S. (2016). Laboratory and field evaluation of the warm fiber reinforced high performance asphalt mixtures (Case study Karaj–Chaloos Road). Construction and Building Materials, 122, 273–283.
-Feng, B., Wang, H., Li, S., Ji, K., Li, L., & Xiong, R. (2022). The durability of asphalt mixture with the action of salt erosion: A review. Construction and Building Materials, 315, 125749.
-Haider, S. W., & Chatti, K. (2009). Effect of design and site factors on fatigue cracking of new flexible pavements in the LTPP SPS-1 experiment. International Journal of Pavement Engineering, 10(2), 133–147.
-Hosseini, S. G., Kordani, A. A., & Zarei, M. (2023). Effect of recycled additives on mode I fracture resistance and moisture susceptibility of HMA. Theoretical and Applied Fracture Mechanics, 128, 104168.
-Jiang, Q., Chen, M., Zhao, Y., Wu, S., Fan, Y., Gan, Z., & Zhang, Y. (2022). Comprehensive assessment of the durability deterioration of asphalt pavement in salt environment: A literature review. Case Studies in Construction Materials, 17, e01706.
-Llopis-Castelló, D., García-Segura, T., Montalbán-Domingo, L., Sanz-Benlloch, A., & Pellicer, E. (2020). Influence of pavement structure, traffic, and weather on urban flexible pavement deterioration. Sustainability, 12(22), 9717.
-Mehdinejad, S., Fazaeli, H., Moniri, A., & Dabiri, A. S. (2021). Comparison of stress intensity factor and fracture energy criteria under combined tensile–shear loading: A statistical approach. Construction and Building Materials, 290, 123230.
-Nian, T., Ge, J., Li, P., Guo, R., Li, J., & Wang, M. (2022). Improved three-dimensional discrete modeling method and anti-cracking properties of asphalt mixture. Construction and Building Materials, 321, 126405.
-Xiong, H., Han, J., Wang, J., Ren, Q., & Wu, L. (2022). Application of high viscosity–high modulus modified asphalt concrete in bus rapid transit station pavement: A case study in Chengdu, China. Case Studies in Construction Materials, 17, e01337.
-Zhang, M., Zhang, J., Lyu, L., Li, Y., Tan, X., Li, Z., & Pei, J. (2024). Durable and environmental asphalt pavement with plant fiber: A state-of-the-art review. Journal of Materials in Civil Engineering, 36(3), 03123003.
-Zhong, T., Zheng, Y., Chen, Z., Yao, L., Zhang, W., Zhu, Y., & Fu, L. (2024). Utilization of steel slag as coarse aggregate and filler in SMA mixture: Engineering performance, environmental impact and economic benefits. Journal of Cleaner Production, 450, 141891.
-Zhou, Z., Li, H., Liu, X., & He, W. (2020). Investigation of sea salt erosion effect on the asphalt–aggregate interfacial system. International Journal of Pavement Research and Technology, 13(2), 145–153.
Journal of Transportation Research
Volume 23, Issue 4
Autumn 2026
Pages 67-78