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Bituminous mixtures containing various defects, such as cracks and voids, exhibit ductile behavior in hot environments and brittle behavior in cold environments. Such materials can only be simulated realistically with the criteria of nonlinear fracture mechanics. In this investigation, the semi-circular bending (SCB) specimens, which have commonly been used to measure linear elastic fracture toughness parameters and fracture energy of asphalt and rock materials up to now, are studied to estimate several nonlinear fracture parameters of bituminous composites such as nonlinear fracture toughness, effective crack extension, critical crack tip opening displacement and brittleness. For this, the compliance functions of SCB specimens based on crack mouth opening displacement and load line displacement are derived by the finite element method at first. Subsequently, eight series of SCB tests in the literature, which were experimented with at low and room temperature environments, are examined by using two different compliance methods in fracture mechanics of quasi-brittle materials. This study's findings reveal that the crack extension in the pre-peak regime for mixes with bituminous at both low and normal temperatures is not statistically negligible and the use of nonlinear fracture toughness is, therefore, a necessity.

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.

Large-scale earthquakes can cause huge loss of life and material losses. After an earthquake, highways are the most commonly used type of transportation for the delivery of the necessary aid teams and materials to the scene of the event. If the highways are not well maintained, it may cause serious disruption of transportation after the earthquake or aftershocks. In this study, field studies were conducted in the provinces where the earthquake was felt severely after the earthquakes in Turkey on February 6, 2023. In these studies, images were collected according to the condition of asphalt cracks on the highways. These images were labeled as in need of urgent maintenance (Major) and not in need of urgent maintenance (Minor) and a new dataset was created. The classification performance of popular pre-trained CNN models is evaluated on this dataset. First, classification algorithms other than softmax were used to improve the classification performance. The Combined Metaheuristic Optimization-Relieff (CMO-R) algorithm was designed to improve the classification performance by one more level. Extensive experiments were conducted on the dataset, and the VGG16 model demonstrated superior performance, reaching an accuracy of 80.32% without encountering overfitting.

Bu çalışmada iki farklı boyutta (No.40 elek üstü ve altı) öğütülmüş araç lastiği (CR) ve öğütülmüş araç lastiğinin pirolizinden elde edilen karbon siyahı bitüm modifikasyonunda 3 farklı oranda (%5, %10 ve %15) kullanılmıştır. Saf ve modifiye bağlayıcılar üzerinde penetrasyon, yumuşama noktası, viskozite ve dinamik kayma reometresi (DSR) deneyleri uygulanmıştır. Ayrıca bağlayıcıların ısıya karşı hassasiyetinin göstergesi olan penetrasyon indeksi (PI) değerleri belirlenmiştir. Penetrasyon, yumuşama noktası ve viskozite deneyleri sonucunda bitüm modifikasyonunda kullanılan CR miktarı arttıkça penetrasyon değerlerinin düzenli olarak azaldığı, yumuşaman noktası ve viskozite değerlerinin düzenli olarak arttığı belirlenmiştir. Bu durum CR kullanımı ile bitümün rijitliğinin arttığını göstermektedir. CR boyutu değerlendirildiğinde malzeme boyutunun bu üç deney üzerinde önemli bir etkiye sahip olmadığı belirlenmiştir. PI değerlerinden saf bağlayıcıya kıyasla No.40 elek altı %5 CR kullanılan modifiye bitüm dışındaki tüm modifiye bitümlerin ısıya karşı hassasiyetinin daha az olduğu belirlenmiştir. DSR deneyleri sonucunda CR içeriği arttıkça bağlayıcıların tekerlek izine karşı dayanım parametrelerinin ve elastikiyetlerinin arttığı belirlenmiştir. Öğütülmüş CR’nin pirolize tabi tutulmasından elde edilen karbon siyahının bitüm modifikasyonunda kullanılması, CR’nin etkinliğini azaltmış özellikle No.40 altı boyuttaki CR’nin karbon siyahı saf bitümün özelliklerini iyileştirme özelliği göstermemiştir.