WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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

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2025 - 20262

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Author: search.filters.author.Ekici, BerkSubject: search.filters.subject.Computational Design

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  • Article
    AI-Supported Seismic Performance Evaluation of Structures: Challenges, Gaps, and Future Directions at Early Design Stages
    (Elsevier Sci Ltd, 2026) Ak, Fatma; Ekici, Berk; Demir, Ugur
    This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.
  • Article
    A Systematic Evaluation of Computational Tools for Layout Generation: The Case of Emergency Departments
    (Taylor & Francis Ltd, 2025) Gulec, Ece; Kasali, Altug; Ekici, Berk
    The configuration of Emergency Department (ED) layouts requires adherence to strict guidelines and codes to achieve certain efficiency, accuracy, and safety measures. Considering the context of ED space planning, integrating computational design can cope with the complexity of floor plan generation due to its rule-based and data-driven nature. This paper presents a comparative analysis of existing plan generation plug-ins to assess their methodological approaches, input requirements, and output effectiveness in ED design. The systematic analysis intends to determine their suitability for automating ED plan generation while ensuring compliance with healthcare guidelines and regulations. The findings highlight the strengths and limitations of various plug-ins, offering insights into their applicability for optimizing spatial configurations, workflow efficiency, and patient flow management. This research contributes to advancing computational design methods in healthcare architecture, demonstrating whether and how automation can enhance precision and efficiency in complex planning tasks.