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- MakaleAçık Erişim2026Scopus SJRQ1“A combined experimental numerical statistical framework to characterize fracture behavior of asphalt mixtures”
Atar, Muhammed; Ince, Ragip; Bakhshizadeh, Majid; Pirmohammad, Sadjad; Furtana Yalcin, Beyza; Yilmaz, Mehmet
This study investigates the fracture and viscoelastic behavior of asphalt mixtures through a combined experimental, numerical, and statistical approach. Semi Circular Bend (SCB) tests were performed on specimens with notch lengths of 10, 20, and 30 mm at two temperature levels (0 °C and room temperature) to determine fracture toughness (KIc), fracture energy, and stiffness parameters. The Extended Finite Element Method (XFEM) was employed to simulate crack initiation and propagation, providing a close match with experimental SCB results under various thermal and geometric conditions. To further interpret the effects of temperature and crack geometry, the Response Surface Methodology (RSM) was applied to the experimental data, enabling the identification of key interaction effects and optimization of fracture parameters. The results demonstrated that decreasing temperature and increasing notch length notably reduced fracture resistance, while XFEM accurately represented the nonlinear crack propagation observed in laboratory testing. Moreover, RSM provided predictive equations that were in strong agreement with XFEM outcomes. The proposed integrated SCB-XFEM-RSM framework presents an efficient and reliable methodology for evaluating and optimizing the fracture performance of asphalt mixtures, offering valuable insights for the design of durable and sustainable pavement systems under diverse environmental conditions.
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İncele - MakaleAçık Erişim2025Scopus SJRQ1Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
Yilmaz, Mehmet; Yalcin, Erkut; Demir, Fatih; Ozdemir, Ahmet Munir; Atar, Muhammed; Gunes, Aysegul; Furtana Yalcin, Beyza; Cambay, Ertugrul
This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.
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