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In recent years, there have been unprecedented developments in artificial intelligence. Object detection, voice recognition, face recognition etc. are some of the artificial intelligence applications. In this study, an auxiliary method for the automatic detection of cracks, one of the main deterioration problems on highways, is proposed. The crack formation of hot mix asphalts is investigated with an image processing method modeled with Attention SegNet architecture. Styrene-butadiene-styrene (SBS), the most widely used additive in bitumen modification, was used at 2 %, 3 %, and 4 % ratios to modify 50/70 bitumen. Semi-circular asphalt specimens obtained with SBS modified bitumen were subjected to a semicircular bending (SCB) test and fracture performance was investigated. The effects of different temperature, notch size and additive on crack detection performance are evaluated. In the experimental study, maximum load, fracture energy, fracture toughness (KIC) values were obtained at low temperature, and resistance values against crack propagation were obtained by applying the J-integral method at intermediate temperature. The results demonstrated that with the addition of SBS, the fracture strength and maximum load values increased at each temperature value, with the 4 % SBS mixture offering the highest performance. Moreover, the image segmentation performed with SegNet provided high accuracy and precision values for cracks. It was observed that the accuracy values of the image processing methods decreased at low temperature, while at high temperature, higher accuracy values were obtained as the cracking rate.

According to the data of the Disaster and Emergency Management (AFAD) Earthquake Department; On February 6, 2023, at 4.17 and 13.24 Turkey time, earthquakes with magnitudes of 7.7 and 7.6 (Mw) occurred, respectively, with the epicenters in Pazarcık (Kahramanmaraş) and Elbistan (Kahramanmaraş). These earthquakes affected 11 provinces of Turkey and were recorded as the most destructive earthquakes in the last century. In the statement made by AFAD on April 20, 2023, it was reported that the number of people who lost their lives in the earthquakes centered in Kahramanmaraş was 50 thousand 96 and the number of people injured was 107 thousand 204. Adıyaman was shaken by the effects of the earthquakes and many buildings were destroyed and damaged. In this study, after the earthquakes, some damaged masonry structures, and in fill walls in Adıyaman city center; the structural irregularities, application methods and material properties used were examined on site and the causes of the damages were evaluated. The damages suffered by historical buildings in the earthquake as a result of the strengthening were discussed. After the buildings were demolished, on-site inspections were carried out.

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.