Atar, Muhammed; Ince, Ozan; Çakıl, Burak; İnce, Elif Gökçe

Earthquakes are natural events that challenge the limits of engineering designs through the forces they exert on structures. The devastating outcomes of the 2023 Kahramanmaraş earthquakes have underscored the urgent need to reassess the seismic safety of the building stock and current design practices. Frame continuity in reinforced concrete buildings is particularly vital for structural strength and earthquake resilience. This study examines the effects of frame discontinuity on the performance of reinforced concrete buildings using nonlinear analysis methods. As part of the research, three regular reinforced concrete buildings were initially designed and later re-modeled to incorporate frame discontinuities. Both building types were analyzed using the nonlinear pushover analysis method, and the resulting capacity curves and damage states were compared to evaluate the impact of frame discontinuity. The analysis results reveal the negative impact of frame continuity on the building performance.

Kulluk, Sinem; Karakaş, Betül

Fast and accurate detection of product defects in manufacturing systems is crucial for cost, quality and customer satisfaction. Surface defects comprise a large portion of production defects and can be successfully detected using deep learning methods. This study employed Convolutional Neural Network (CNN) architectures to classify different types of surface defects and examine the effects of different architectures on various surface types. Four datasets (Metal, Wood, Food, and Fabric) were classified using three CNN architectures: GoogleNet, SqueezeNet, and VGG 19. Validation accuracy was considered in the classification process. According to the results, for the grayscale Metal and Fabric datasets, the GoogleNet architecture achieved higher classification accuracies than the other architectures, with 99,89% and 96,86%, respectively. In contrast, for the color datasets, namely Wood and Food, the VGG19 architecture demonstrated higher classification performance, achieving 94,39% and 99,96%, respectively. As a result of this study, the classification accuracies obtained for the Wood and Fabric datasets were above the average reported in the literature, while the accuracies for the Metal and Food datasets surpassed those reported in previous studies.