WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Publisher: search.filters.publisher.Ecaade-education & Research Computer Aided Architectural design Europe

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  • Conference Object
    Decoding and Predicting the Attributes of Urban Public Spaces With Soft Computing Models and Space Syntax Approaches
    (Ecaade-education & Research Computer Aided Architectural design Europe, 2023) Yonder, Veli Mustafa; Dogan, Fehmi; Cavka, Hasan Burak; Tayfur, Gokmen; Dulgeroglu, Ozum
    People spend a considerable amount of time in public spaces for a variety of reasons, albeit at various times of the day and during season. Therefore, it is of utmost importance for both urban designers and local authorities to try to gain an understanding of the architectural qualities of these spaces. Within the scope of this study, squares and green parks in Izmir, the third largest city in Turkey, were analyzed in terms of their dimensions, landscape characteristics, the quality of their semi-open spaces, their landmarks, accessibility, and overall aesthetic quality. Using linear predictor, general regression neural networks, multilayer feed-forward neural networks (2-3-4-5-6 nodes), and genetic algorithms, soft computing models were trained in accordance with the results of the conducted analyses. Meanwhile, using space syntax methodologies, a visibility graph analysis and axial map analysis were conducted. The training results (i.e., root mean square error, mean absolute error, bad prediction rates for testing and training phases, and standard deviation of absolute error) were obtained in a comparative table based on training times and root mean square error values. According to the benchmarking table, the network that most accurately predicts the aesthetic score is the 2-node MLFNN, whereas the 6-node MLFN network is the least successful network.
  • Conference Object
    The Role of the Computational Designer From Computer-Aided Design To Machine Learning-Aided Design a Study on Generative Models and Design Prompts
    (Ecaade-education & Research Computer Aided Architectural design Europe, 2023) Yonder, Veli Mustafa; Dulgeroglu, Ozum; Dogan, Fehmi; Cavka, Hasan Burak
    The rising sophistication of digital design technologies and instruments requires computational designers to acquire a broader set of abilities, such as expertise in a variety of digital models, scripting languages, and the ability to manage complicated data models. In the field of design, the concepts of machine learning-aided design and data-driven techniques contribute to the production of various and numerous design possibilities. Ultimately, this will lead the computational designer to redefine his or her power over the design protocol. In this paper, ChatGPT-3.5, Dall-E v2, and Stable Diffusion, cutting-edge artificial intelligence models, are used to construct sample design scenarios. Using a text mining application, the scenario-specific prompts were examined to explore these models' computational design potential.