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Journals / Journal of Computational Design / 2026 / Cilt: 6 Sayı: 1

Generative Adversarial Networks (GANs), and Architecture: Investigating Quality in Architectural Plan Generation

Yapay Zekâ, Çekişmeli Üretken Ağlar (ÇÜA) ve Mimarlık: Mimari Plan Üretiminde Nitelik Araştırılması

Özlem Gök Tokgöz(Eskişehir Teknik Üniversitesi)0000-0002-1702-0126
Mehmet Ali Altın(Eskişehir Teknik Üniversitesi)0000-0001-8992-7088
Pages
191–210
Publication Date
2025-03-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study concludes by underlining the limited capacity of the GANs to enhance the quality of architectural plans, and provides comments and reviews on this matter.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial intelligence (AI) finds extensive applications in architecture, alongside various other domains of daily life.
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Key Findings & Data:Consequently, evaluating the quality of plans generated through AI poses a novel and contemporary challenge.
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Conclusion & Impact:This study concludes by underlining the limited capacity of the GANs to enhance the quality of architectural plans, and provides comments and reviews on this matter.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Mimari Plan Üretimi · Mimari Planda Nitelik

Abstract

Artificial intelligence (AI) finds extensive applications in architecture, alongside various other domains of daily life. Recent years have witnessed a surge in visual processing, analysis, and production, primarily propelled by deep learning algorithms. Among these algorithms, Generative Adversarial Networks (GANs) stand out as exemplary tools for image generation. Within architecture, GANs are utilized across various domains including facade design, interior layout, and generation of perspectives and architectural plans. Notably, GANs have emerged as prominent tools in architectural plan generation. However, unlike other image synthesis tasks, architectural plan generation places greater emphasis on plan quality over image fidelity. Consequently, evaluating the quality of plans generated through AI poses a novel and contemporary challenge. While some studies touch upon quality issues in GAN-generated outputs, a comprehensive exploration of quality-related concerns remains lacking. The study analyses the plans generated by GAN and assesses the capacity of GAN in ensuring architectural quality. To this purpose, a study is undertaken to analyze existing architectural plan generation studies utilizing GANs and to interpret the notion of architectural quality. The studies analyzed that the experiments with GANs are preliminary and there is a lack of studies on the production of higher quality plans using GANs. However, these studies seem to be due to the limitations of GAN itself. This study concludes by underlining the limited capacity of the GANs to enhance the quality of architectural plans, and provides comments and reviews on this matter.

Özet

Yapay zekâ uygulamaları hayatımızın her alanında olduğu gibi mimarlık alanında da aktif kullanılmaktadır. Günümüzde özellikle derin öğrenme algoritmaları yardımıyla görsel işleme, analiz ve üretimleri artmıştır. Derin öğrenmenin bir türü olan, ÇÜA (Çekişmeli Üretken Ağ) görsel üretimi üzerine en iyi örnekler veren algoritmalardan biridir. Görsel tabanlı bir algoritma olan ÇÜA başta görsel üretimi olmak üzere, görüntüden görüntüye, metinden görüntüye, fotoğraflardan çizme gibi birçok uygulaması bulunmaktadır. Mimarlık alanında ise, cephe, iç mekân, perspektif ve plan üretimleri gibi birçok alanda kullanılmaktadır. Özellikle mimari plan üretiminde öne çıkan bir algoritma olmuştur. Fakat mimari plan üretiminin diğer görsel üretimlerden farklı olarak görselin kalitesinden çok niteliğinin önemli olmasıdır. Bu sebeple yapay zekâ ile üretilen planların niteliği ve bu niteliğin değerlendirilmesi konusu yeni ve güncel bir problemdir. ÇÜA ile yapılan çalışmalarda da niteliğe dair problemler kısmen dile getirilmektedir. Çalışmada ÇÜA algoritmasının mimari plana dair nitelik problemlerine ne ölçüde cevap verebileceğinin araştırılması hedeflenmiştir. Bu kapsamda öncelikle ÇÜA algoritması kullanılarak üretilen mimari plan üretim çalışmaları incelenmiş ve mimari nitelik kavramı araştırılmıştır. Mimarı niteliğe dair elde edilen literatür sonucunda GAN algoritmasının mimari plan niteliğini arttırmak konusunda potansiyelleri tartışılmış ve değerlendirilmiştir.

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Cite This Article

Tokgöz, Ö. G., Altın, M. A. (2025). Generative Adversarial Networks (GANs), and Architecture: Investigating Quality in Architectural Plan Generation. Journal of Computational Design, Cilt: 6 Sayı: 1, 191–210. https://doi.org/10.53710/jcode.1448847

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