Semnan University Press Journal of Rehabilitation in Civil Engineering 2345-4415 13 1 2025 02 01 Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique 66 78 8907 10.22075/jrce.2024.33544.2023 EN Gholamali Shafabakhsh Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran Mostafa Sadeghnejad Assistant Professor, Department of Civil Engineering, Faculty of Technology and Engineering, University of Guilan, Rasht, Iran Milad Keneshlou M.Sc., Faculty of Civil Engineering, Semnan University, Semnan, Iran Journal Article 2024 03 14 Low-temperature cracking (LTC) is a critical form of pavement distress in cold regions. The fracture toughness in the semicircular bending (SCB) test serves as an indicator of LTC growth. Firstly, this study evaluated the effect of adding nano Al₂O₃ on the improvement of hot mix asphalt (HMA) fracture toughness. Another goal of the paper was to investigate the influence of different parameters, such as temperature (-5, -15, and -25 °C), loading mode (I, II, and I/II), crack geometry (vertical and angular cracks), and nano-modification, on the fracture toughness of HMA by using machine learning technique. An artificial neural network (ANN) was employed to quantify the impact of these parameters. The findings of this research clearly show that although asphalt mixtures in cold region are prone to thermal cracks, the addition of nano Al₂O₃ improves their resistance by 12% in comparison with control mixtures. The ANN analysis identified loading mode is the most significant factor affecting fracture toughness (48% contribution). Temperature followed with a 28% contribution, while crack geometry and nano Al₂O₃ modification each contributed 12%. https://civiljournal.semnan.ac.ir/article_8907_be7e9ee7eeb5ee863274186a3fd7ba0e.pdf