Browsing by Author "Karadag I."

Now showing 1 - 4 of 4
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    EDU-AI: a twofold machine learning model to support classroom layout generation
    (Emerald Publishing, 2023) Karadag I.; Güzelci O.Z.; Alaçam S.
    Purpose: This study aims to present a twofold machine learning (ML) model, namely, EDU-AI, and its implementation in educational buildings. The specific focus is on classroom layout design, which is investigated regarding implementation of ML in the early phases of design. Design/methodology/approach: This study introduces the framework of the EDU-AI, which adopts generative adversarial networks (GAN) architecture and Pix2Pix method. The processes of data collection, data set preparation, training, validation and evaluation for the proposed model are presented. The ML model is trained over two coupled data sets of classroom layouts extracted from a typical school project database of the Ministry of National Education of the Republic of Turkey and validated with foreign classroom boundaries. The generated classroom layouts are objectively evaluated through the structural similarity method (SSIM). Findings: The implementation of EDU-AI generates classroom layouts despite the use of a small data set. Objective evaluations show that EDU-AI can provide satisfactory outputs for given classroom boundaries regardless of shape complexity (reserved for validation and newly synthesized). Originality/value: EDU-AI specifically contributes to the automation of classroom layout generation using ML-based algorithms. EDU-AI’s two-step framework enables the generation of zoning for any given classroom boundary and furnishing for the previously generated zone. EDU-AI can also be used in the early design phase of school projects in other countries. It can be adapted to the architectural typologies involving footprint, zoning and furnishing relations. © 2022, Ilker Karadag, Orkan Zeynel Güzelci and Sema Alaçam.
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    A machine learning-based prediction model for architectural heritage: The case of domed Sinan mosques
    (Elsevier Ltd, 2024) Güzelci O.Z.; Alaçam S.; Bekiroğlu B.; Karadag I.
    This study presents a machine learning-based prediction model (PM) customized to predict missing components of historical mosques. Domed mosques built by Architect Sinan during the Classical Ottoman Period (16th century) are selected due to their distinctive features and stylistic similarities. The model development process includes data collection (46 domed Sinan mosques), data preparation and refinement, training, testing, and validation. The Pix2Pix method is used to train and validate the machine learning models, and the Structural Similarity (SSIM) metric is used to objectively evaluate the outcomes. Preliminary results indicate that the success of the PMs is not directly proportional to the number of input components. Instead, factors such as overall mass organization, the curvature of the dome, and the number of balconies on the minaret play crucial roles in determining the success of the outcomes. © 2024 Elsevier Ltd
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    Revisiting the key components of creativity through generative AI
    (IGI Global, 2024) Güzelci O.Z.; Karadag I.
    Today, advancements in artificial intelligence (AI) and its applications have resulted in its widespread use in creative industries. The goal of this study is to investigate the relationship between artificial intel¬ligence and creativity through the perspective of Generative AI (GenAI) utilized in these industries. To perform this investigation, the study first introduces the concepts of creativity and its key components, along with GenAI and AI creativity. The study's analysis is founded on 14 key components of creativ¬ity identified in the previous literature. In the analysis section, the study examines whether these key components are present in today's GenAI tools, drawing on current debates about AI and AI-based ap¬plications. Additionally, the capabilities, limitations, and challenges of GenAI are investigated for each key component. In the discussion section, the study makes projections about potential problems that may be encountered when using GenAI and discusses possible redefinitions of key components in the future. © 2024, IGI Global. All rights reserved.
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    Machine Learning for Pedestrian-Level Wind Comfort Analysis
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Gür M.; Karadag I.
    (1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies. © 2024 by the authors.