Yapay Zekâ Destekli Tasarım Otomasyonu: Mimarlıkta Güncel Yaklaşımlar ve Uygulamalar
Yıl 2025,
Cilt: 41 Sayı: 2, 674 - 684, 30.08.2025
Fazil Akdağ
,
Fatma Betül Künyeli
Öz
Bu çalışma, mimarlıkta yapay zekâ (YZ) destekli tasarım otomasyonuna yönelik son yaklaşımları inceleyerek, tasarım sürecinin çeşitli aşamalarındaki etkisine odaklanmaktadır. Niteliksel, literatür temelli bir metodoloji kullanarak, GAN'lar, difüzyon modelleri ve grafik tabanlı sistemleri içeren akademik kaynaklar ve vaka çalışmalarını analiz eder. Bulgular, yapay zekanın verimliliği artırdığını, yaratıcılığı desteklediğini ve özellikle kavramsal tasarım, düzen oluşturma, form geliştirme ve performans optimizasyonu alanlarında yinelemeli keşfi hızlandırdığını göstermektedir. Bu avantajlara rağmen, veri bağımlılığı, algoritmik istikrarsızlık ve şeffaflık eksikliği gibi sınırlamalar kritik zorluklar olarak kalmaktadır. Çalışma, mevcut uygulamaları dört ana tema altında kategorize eder ve en başarılı sonuçların insan yaratıcılığı ile algoritmik zekayı birleştiren hibrit iş akışlarından doğduğunu vurgular. Yapay zekâ okuryazarlığı ve etik çerçevelerin önemini vurgulayan araştırma, mimari tasarımda yapay zekanın dönüştürücü rolünün daha geniş bir anlayışına katkıda bulunmakta ve profesyonel uygulamalara daha etkili ve sorumlu bir entegrasyon için yönler önermektedir.
Kaynakça
-
Li, Y.; Chen, H.; Yu, P.; Yang (2025). L. A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency. Appl. Sci. 15, 1476. https://doi.org/10.3390/app15031476.
-
Zhuang, X., Ju, Y., Yang, A., & Caldas, L. (2023). Synthesis and generation for 3D architecture volume with generative modeling. International Journal of Architectural Computing, 21(2), 297-314. https://doi.org/10.1177/14780771231168233.
-
RIBA (2024). Artificial Intelligence in Architecture – Insights from the Royal Institute of British Architects Survey.
-
Li, C., Zhang, T., Du, X., Zhang, Y., & Xie, H. (2024). Generative AI Models for Different Steps in Architectural Design: A Literature Review. Frontiers of Architectural Research. https://doi.org/10.48550/arXiv.2404.01335
-
Luo, Z., & Huang, W. (2022). FloorplanGAN: Vector residential floorplan adversarial generation. Automation in Construction, 142, 104470. https://doi.org/10.1016/j.autcon.2022.104470.
-
Aalaei, M., Saadi, M., Rahbar, M., & Ekhlassi, A. (2023). Architectural layout generation using a graph-constrained conditional Generative Adversarial Network (GAN). Automation in Construction, 155, 105053. https://doi.org/10.1016/j.autcon.2023.105053.
-
Del Campo, M., and Leach, N., 2022. Can Machines Hallucinate Architecture? AI as Design Method. Architectural Design, 92(3), pages 6–13. https://doi.org/10.1002/ad.2807.
-
Stigsen, M. B., Moisi, A., Rasoulzadeh, S., Schinegger, K., & Rutzinger, S. (2023). AI Diffusion as Design Vocabulary- Investigating the use of AI image generation in early architectural design and education. eCAADe Proceedings, 2, 587–596. https://doi.org/10.52842/conf.ecaade.2023.2.587.
-
Hu, R., Huang, Z., Tang, Y., van Kaick, O., Zhang, H., & Huang, H. (2020). Graph2Plan: Learning Floorplan Generation from Layout Graphs. ACM Transactions on Graphics (TOG), 39(4), Article 1. https://doi.org/10.1145/3386569.3392391.
-
Cai, Z., Lin, Y., Li, J., Zhang, Z., & Huang, X. (2021). Building facade completion using semantic-synchronized GAN. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6387–6390). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554453.
-
Pan, Y., Shen, Y. X., Qin, J. J., & Zhang, L. M. (2024). Deep reinforcement learning for multi-objective optimization in BIM-based green building design. Automation in Construction, 166, 105598. https://doi.org/10.1016/j.autcon.2024.105598.
-
Wong, S. L., Wan, K. K. W., & Lam, T. N. T. (2010). Artificial Neural Networks for energy analysis of office buildings with daylighting. Applied Energy, 87(2), 551–557. https://doi.org/10.1016/j.apenergy.2009.06.028.
-
Kazemzadeh Azad, S., Aminbakhsh, S., & Gandomi, A. H. (2023). An enhanced guided stochastic search with repair deceleration mechanism for very high-dimensional optimization problems of steel double-layer grids. Structural and Multidisciplinary Optimization, 68(3), 1025–1048. https://doi.org/10.1007/s00158-024-03898-5.
-
Caldas, L. (2006). GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_12.
-
Vahdatikhaki, F., Salimzadeh, N., & Hammad, A. (2022). Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach. Energy and Buildings, 258, 111787. https://doi.org/10.1016/j.enbuild.2021.111787.
-
Moayedi, H., Bui, D. T., Dounis, A., Lyu, Z., & Foong, L. K. (2019). Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques. Applied Sciences, 9(20), 4338. https://doi.org/10.3390/app9204338.
-
Hanafy, N. O. (2023). Artificial intelligence’s effects on design process creativity: A study on used A.I. Text-to Image in architecture. Journal of Building Engineering, 80, 107999. https://doi.org/10.1016/j.jobe.2023.107999.
-
Petráková, L., & Šimkovič, V. (2023). Architectural alchemy: Leveraging Artificial Intelligence for inspired design—a comprehensive study of creativity, control, and collaboration. ALFA: Architecture Papers of the Faculty of Architecture and Design STU, 28(3), 20–35. https://doi.org/10.2478/alfa-2023-0020.
-
Tan, L., & Luhrs, M. (2024). Using Generative AI Midjourney to enhance divergent thinking in architectural design studio. The Design Journal, 27(1), 47–61. https://doi.org/10.1080/14606925.2024.2353479.
-
mpanavos, S., Malkawi, A. (2022). Early-Phase Performance-Driven Design Using Generative Models. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_6.
-
Geyer, P., Singh, M. M., & Chen, X. (2024). Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning demonstrated by energy-efficient building design. Advanced Engineering Informatics, 62, 102843. https://doi.org/10.1016/j.aei.2024.102843.
-
Durmuş, D., Giretti, A., Ashkenazi, O., Carbonari, A., & Isaac, S. (2024). The role of large language models for decision support in fire safety planning. Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), 345–352. https://doi.org/10.22260/ISARC2024/0045.
-
Zhang, J., & El-Gohary, N. M. (2022). BIM, NLP, and AI for automated compliance checking. In M. Wang, A. P. Liew, & A. Borrmann (Eds.), Proceedings of the ASCE International Conference on Computing in Civil Engineering 2022 (pp. 103–110). https://doi.org/10.4337/9781839105524.00022.
-
Zhuang, X., Zhu, P., Yang, A., & Caldas, L. (2025). Machine learning for generative architectural design: Advancements, opportunities, and challenges. Automation in Construction, 174, 106129. https://doi.org/10.1016/j.autcon.2025.106129.
-
Vissers-Similon, E., Dounas, T., & De Walsche, J. (2024). Classification of artificial intelligence techniques for early architectural design stages. International Journal of Architectural Computing. https://doi.org/10.1177/14780771241260857.
-
Gomes, G. F., Bendine, K., & Pereira, J. L. J. (2025). Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design. Mechanics Based Design of Structures and Machines, 1–28. https://doi.org/10.1080/15397734.2025.2476041.
-
Özorhon, G., Gelirli, D. N., Lekesiz, G., & Müezzinoğlu, C. (2025). AI-assisted architectural design studio (AI-a-ADS): How artificial intelligence join the architectural design studio? International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-025-09975-0.
-
Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024). Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology. The Egyptian Journal of Radiology and Nuclear Medicine, 55(1). https://doi.org/10.1186/s43055-024-01356-2.
-
NCARB (2023). NCARB’s Position on the Use of Artificial Intelligence in the Architectural Profession. National Council of Architectural Registration Boards
-
Gaffar, H., & Albarashdi, S. (2023). Copyright Protection for AI-Generated Works: Exploring Originality and Ownership in a Digital Landscape. Asian Journal of International Law, 13(1), 1–22. https://doi.org/10.1017/S2044251323000735.
-
Santoni de Sio, F. (2024). Artificial Intelligence and the Future of Work: Mapping the Ethical Issues. The Journal of Ethics, 28(3), 407–427. https://doi.org/10.1007/s10892-024-09493-6.
-
Gradišar, L., Dolenc, M., & Klinc, R. (2024). Towards machine learned generative design. Automation in Construction, 159, 105284. https://doi.org/10.1016/j.autcon.2024.105284.
Artificial Intelligence Supported Design Automation: Current Approaches and Applications in Architecture
Yıl 2025,
Cilt: 41 Sayı: 2, 674 - 684, 30.08.2025
Fazil Akdağ
,
Fatma Betül Künyeli
Öz
This study investigates recent approaches to artificial intelligence (AI)-supported design automation in architecture, focusing on its impact across various stages of the design process. Using a qualitative, literature-based methodology, it analyzes academic sources and case studies involving GANs, diffusion models, and graph-based systems. Findings show that AI enhances efficiency, supports creativity, and accelerates iterative exploration, particularly in conceptual design, layout generation, form development, and performance optimization. Despite these advantages, limitations such as data dependency, algorithmic instability, and lack of transparency remain critical challenges. The study categorizes current applications into four key themes and highlights that the most successful outcomes arise from hybrid workflows combining human creativity and algorithmic intelligence. Emphasizing the importance of AI literacy and ethical frameworks, the research contributes to a broader understanding of AI’s transformative role in architectural design. It suggests directions for more effective and responsible integration into professional practice.
Etik Beyan
Bu çalışma, herhangi bir insan katılımcı, hayvan deneyi veya özel veri kullanımı içermemektedir. Araştırma yalnızca açık erişimli bilimsel kaynakların incelenmesine dayanmaktadır. Bu nedenle, etik kurul onayı veya katılımcı onamı gerekmemektedir. Çalışma araştırma ve yayın etiği ilkelerine uygun olarak yürütülmüştür.
Kaynakça
-
Li, Y.; Chen, H.; Yu, P.; Yang (2025). L. A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency. Appl. Sci. 15, 1476. https://doi.org/10.3390/app15031476.
-
Zhuang, X., Ju, Y., Yang, A., & Caldas, L. (2023). Synthesis and generation for 3D architecture volume with generative modeling. International Journal of Architectural Computing, 21(2), 297-314. https://doi.org/10.1177/14780771231168233.
-
RIBA (2024). Artificial Intelligence in Architecture – Insights from the Royal Institute of British Architects Survey.
-
Li, C., Zhang, T., Du, X., Zhang, Y., & Xie, H. (2024). Generative AI Models for Different Steps in Architectural Design: A Literature Review. Frontiers of Architectural Research. https://doi.org/10.48550/arXiv.2404.01335
-
Luo, Z., & Huang, W. (2022). FloorplanGAN: Vector residential floorplan adversarial generation. Automation in Construction, 142, 104470. https://doi.org/10.1016/j.autcon.2022.104470.
-
Aalaei, M., Saadi, M., Rahbar, M., & Ekhlassi, A. (2023). Architectural layout generation using a graph-constrained conditional Generative Adversarial Network (GAN). Automation in Construction, 155, 105053. https://doi.org/10.1016/j.autcon.2023.105053.
-
Del Campo, M., and Leach, N., 2022. Can Machines Hallucinate Architecture? AI as Design Method. Architectural Design, 92(3), pages 6–13. https://doi.org/10.1002/ad.2807.
-
Stigsen, M. B., Moisi, A., Rasoulzadeh, S., Schinegger, K., & Rutzinger, S. (2023). AI Diffusion as Design Vocabulary- Investigating the use of AI image generation in early architectural design and education. eCAADe Proceedings, 2, 587–596. https://doi.org/10.52842/conf.ecaade.2023.2.587.
-
Hu, R., Huang, Z., Tang, Y., van Kaick, O., Zhang, H., & Huang, H. (2020). Graph2Plan: Learning Floorplan Generation from Layout Graphs. ACM Transactions on Graphics (TOG), 39(4), Article 1. https://doi.org/10.1145/3386569.3392391.
-
Cai, Z., Lin, Y., Li, J., Zhang, Z., & Huang, X. (2021). Building facade completion using semantic-synchronized GAN. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6387–6390). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554453.
-
Pan, Y., Shen, Y. X., Qin, J. J., & Zhang, L. M. (2024). Deep reinforcement learning for multi-objective optimization in BIM-based green building design. Automation in Construction, 166, 105598. https://doi.org/10.1016/j.autcon.2024.105598.
-
Wong, S. L., Wan, K. K. W., & Lam, T. N. T. (2010). Artificial Neural Networks for energy analysis of office buildings with daylighting. Applied Energy, 87(2), 551–557. https://doi.org/10.1016/j.apenergy.2009.06.028.
-
Kazemzadeh Azad, S., Aminbakhsh, S., & Gandomi, A. H. (2023). An enhanced guided stochastic search with repair deceleration mechanism for very high-dimensional optimization problems of steel double-layer grids. Structural and Multidisciplinary Optimization, 68(3), 1025–1048. https://doi.org/10.1007/s00158-024-03898-5.
-
Caldas, L. (2006). GENE_ARCH: An Evolution-Based Generative Design System for Sustainable Architecture. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_12.
-
Vahdatikhaki, F., Salimzadeh, N., & Hammad, A. (2022). Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach. Energy and Buildings, 258, 111787. https://doi.org/10.1016/j.enbuild.2021.111787.
-
Moayedi, H., Bui, D. T., Dounis, A., Lyu, Z., & Foong, L. K. (2019). Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques. Applied Sciences, 9(20), 4338. https://doi.org/10.3390/app9204338.
-
Hanafy, N. O. (2023). Artificial intelligence’s effects on design process creativity: A study on used A.I. Text-to Image in architecture. Journal of Building Engineering, 80, 107999. https://doi.org/10.1016/j.jobe.2023.107999.
-
Petráková, L., & Šimkovič, V. (2023). Architectural alchemy: Leveraging Artificial Intelligence for inspired design—a comprehensive study of creativity, control, and collaboration. ALFA: Architecture Papers of the Faculty of Architecture and Design STU, 28(3), 20–35. https://doi.org/10.2478/alfa-2023-0020.
-
Tan, L., & Luhrs, M. (2024). Using Generative AI Midjourney to enhance divergent thinking in architectural design studio. The Design Journal, 27(1), 47–61. https://doi.org/10.1080/14606925.2024.2353479.
-
mpanavos, S., Malkawi, A. (2022). Early-Phase Performance-Driven Design Using Generative Models. In: Gerber, D., Pantazis, E., Bogosian, B., Nahmad, A., Miltiadis, C. (eds) Computer-Aided Architectural Design. Design Imperatives: The Future is Now. CAAD Futures 2021. Communications in Computer and Information Science, vol 1465. Springer, Singapore. https://doi.org/10.1007/978-981-19-1280-1_6.
-
Geyer, P., Singh, M. M., & Chen, X. (2024). Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning demonstrated by energy-efficient building design. Advanced Engineering Informatics, 62, 102843. https://doi.org/10.1016/j.aei.2024.102843.
-
Durmuş, D., Giretti, A., Ashkenazi, O., Carbonari, A., & Isaac, S. (2024). The role of large language models for decision support in fire safety planning. Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), 345–352. https://doi.org/10.22260/ISARC2024/0045.
-
Zhang, J., & El-Gohary, N. M. (2022). BIM, NLP, and AI for automated compliance checking. In M. Wang, A. P. Liew, & A. Borrmann (Eds.), Proceedings of the ASCE International Conference on Computing in Civil Engineering 2022 (pp. 103–110). https://doi.org/10.4337/9781839105524.00022.
-
Zhuang, X., Zhu, P., Yang, A., & Caldas, L. (2025). Machine learning for generative architectural design: Advancements, opportunities, and challenges. Automation in Construction, 174, 106129. https://doi.org/10.1016/j.autcon.2025.106129.
-
Vissers-Similon, E., Dounas, T., & De Walsche, J. (2024). Classification of artificial intelligence techniques for early architectural design stages. International Journal of Architectural Computing. https://doi.org/10.1177/14780771241260857.
-
Gomes, G. F., Bendine, K., & Pereira, J. L. J. (2025). Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design. Mechanics Based Design of Structures and Machines, 1–28. https://doi.org/10.1080/15397734.2025.2476041.
-
Özorhon, G., Gelirli, D. N., Lekesiz, G., & Müezzinoğlu, C. (2025). AI-assisted architectural design studio (AI-a-ADS): How artificial intelligence join the architectural design studio? International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-025-09975-0.
-
Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M. (2024). Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology. The Egyptian Journal of Radiology and Nuclear Medicine, 55(1). https://doi.org/10.1186/s43055-024-01356-2.
-
NCARB (2023). NCARB’s Position on the Use of Artificial Intelligence in the Architectural Profession. National Council of Architectural Registration Boards
-
Gaffar, H., & Albarashdi, S. (2023). Copyright Protection for AI-Generated Works: Exploring Originality and Ownership in a Digital Landscape. Asian Journal of International Law, 13(1), 1–22. https://doi.org/10.1017/S2044251323000735.
-
Santoni de Sio, F. (2024). Artificial Intelligence and the Future of Work: Mapping the Ethical Issues. The Journal of Ethics, 28(3), 407–427. https://doi.org/10.1007/s10892-024-09493-6.
-
Gradišar, L., Dolenc, M., & Klinc, R. (2024). Towards machine learned generative design. Automation in Construction, 159, 105284. https://doi.org/10.1016/j.autcon.2024.105284.