Shafabakhsh, Gholamali, Sadeghnejad, Mostafa, Keneshlou, Milad. (1403). Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique. هنرهای کاربردی, 13(1), 66-78. doi: 10.22075/jrce.2024.33544.2023
Gholamali Shafabakhsh; Mostafa Sadeghnejad; Milad Keneshlou. "Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique". هنرهای کاربردی, 13, 1, 1403, 66-78. doi: 10.22075/jrce.2024.33544.2023
Shafabakhsh, Gholamali, Sadeghnejad, Mostafa, Keneshlou, Milad. (1403). 'Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique', هنرهای کاربردی, 13(1), pp. 66-78. doi: 10.22075/jrce.2024.33544.2023
Shafabakhsh, Gholamali, Sadeghnejad, Mostafa, Keneshlou, Milad. Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique. هنرهای کاربردی, 1403; 13(1): 66-78. doi: 10.22075/jrce.2024.33544.2023

Research and Comparison of Nano-Asphalt Mixture Fracture Toughness Based on Machine Learning Technique

Journal of Rehabilitation in Civil Engineering
مقاله 5، دوره 13، شماره 1 - شماره پیاپی 37، بهار 2025، صفحه 66-78    اصل مقاله (1.22 M)
نوع مقاله: Regular Paper
شناسه دیجیتال (DOI): 10.22075/jrce.2024.33544.2023
نویسندگان
Gholamali Shafabakhsh1؛ Mostafa Sadeghnejad* 2؛ Milad Keneshlou3
1Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran
2Assistant Professor, Department of Civil Engineering, Faculty of Technology and Engineering, University of Guilan, Rasht, Iran
3M.Sc., Faculty of Civil Engineering, Semnan University, Semnan, Iran
چکیده
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%.

تازه های تحقیق
  • Low-temperature cracking (LTC) is a critical pavement distress in cold regions.
  • Fracture toughness in SCB test is an indicator of LTC growth.
  • A 12% increase in fracture toughness has been observed by adding 0.6% nano Al2O3 to asphalt binder.
  • Based on ANN analysis, loading mode has the most effect on fracture toughness (48% contribution).
کلیدواژه‌ها
Asphalt mixture؛ Low temperature crack؛ Cold region؛ fracture toughness؛ Machine learning technique
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