In this paper, a fuzzy logic algorithm was created in order to grade and classify the design efficiencies of classrooms selected from Süleyman Demirel University. The existing classrooms were examined on site and the orientation of the classrooms, the number of people, the classroom area and the window area of the classroom were calculated. As the input variables, the orientation of the classrooms, the number of people, the area per-capita and the ratio of window area to the classroom area were modelled. The design efficiencies of the classrooms as the output variables were obtained by the rules formed among the input variables. In the model, fuzzy model as the Mamdani type and "weighted average" method as the clarification method were used. For fuzzy logic model, 180 fuzzy rules have been formed in the type of IF, which are associated with the facade of the classroom, the number of people, the area per capita and the ratio of the window area of the classrooms to the design efficiency of the classroom. Design efficiency of the classrooms were created; the design efficiency classes and the average design efficiencies of classrooms in faculties were compared and concluded according to faculties. The efficiency of the classrooms, which is the main place of the educational buildings, depends on the decisions taken during the design phase. With the model created in this paper, more efficient designs will be provided by having knowledge about the design efficiency through making use of the decision-making process of the classrooms during the design process.
In this study, it is aimed to develop an adaptive network-based fuzzy inference system (ANFIS) model that can estimate tread width values based on the physical characteristics of people and suitable for comfort and safety needs. The input values were obtained by measuring the height, step, and shoe sole lengths of the sample group of 200 people. For the tread width value to be used as output value, a prototype stair model in which different step sizes can be experienced was used. The tread width value obtained by using the test data in the developed ANFIS model was compared with the tread width value obtained from the experimental study. It has been concluded that the ANFIS model developed as a result of the comparison can be used as an efficient tool in estimating the value of stair tread width, which can meet people’s physical comfort needs.
Unlike traditional machine learning methods, deep learning methods that can learn from image, video, audio, and text data, especially recently with the increase in hardware power, are also increasing in success. Considering the success and benefits of deep learning methods in many different fields with increasing data, similar effects are expected in architecture. In this study, we focused on textures by going down to specifics rather than general images. In this direction, a total of 4500 satellite images belonging to cloud, desert, green areas and water bodies were classified in the model developed using deep convolutional neural networks. In the developed model, 0.97 accuracy for cloud images, 0.98 accuracy for desert images, 0.96 accuracy for green areas images and 0.98 accuracy for water bodies images were obtained in the classification of previously unused test data (675 images). Although there are similarities in the images of cloud and desert, and images of green areas and water bodies, this success in textures shows that it can be successful in detecting, analyzing, and classifying architectural materials. Successful recognition, analysis and classification of architectural materials and elements with deep convolutional neural networks will be able to facilitate the acquisition of appropriate and useful data through shape recognition among many data, especially at the information collection phase in the architectural design process. Thus, it will help to take more accurate decisions by obtaining more comprehensive data that cannot be obtained from manual data analysis. Learning the distinctive features for classification of data in deep convolutional neural networks also explains architectural design differences and similarities. This situation reveals the hidden relationship in the designs and thus can offer architects the opportunity to make creative and original designs.
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