A RESIDENTIAL EXPERIENCE AT THE INTERSECTION OF AI-ENABLED DESIGN AND INDUSTRIAL CONSTRUCTION: A CASE STUDY OF PROJECT PHOENIX

Abstract

This study examines the interaction between artificial intelligence–enabled design processes and industrial construction techniques in housing architecture through the lens of Project Phoenix, developed in West Oakland, California. As the discipline of architecture confronts multilayered challenges such as the global housing crisis, climate change, and accelerating digitalization, artificial intelligence, generative algorithms, and modular production systems are assuming transformative roles within design workflows. Project Phoenix was conceived using AI-supported analyses integrated via platforms like Autodesk Forma, while its delivery relies on modular, rapid-assembly construction systems developed by Factory_OS. The project further advances sustainability by employing carbon-negative façade panels that combine mycelium-based biomaterials with fiber-reinforced polymer (FRP) technology, and simultaneously achieves a dramatic reduction in building time compared to conventional methods. This case study offers a multi-dimensional evaluation of Project Phoenix by systematically investigating its architectural reasoning, production technologies, material strategies, contextual engagement, and sustainability interventions. Through this analysis, the research aims to surface transferable insights and serve as an exemplar for future architectural applications. Moreover, the study proposes an integrated theoretical and practical framework for understanding how digitalized design processes and industrialized construction paradigms can jointly reshape the production of the built environment.

Keywords

• AI-Enabled Design
• Industrial Construction
• Sustainable Architecture
• Modular Housing Systems
• Case Study

YAPAY ZEKÂ DESTEKLİ TASARIM VE ENDÜSTRİYEL YAPIMIN KESİŞİMİNDE BİR KONUT DENEYİMİ: PROJECT PHOENİX VAKA ANALİZİ

Abstract

Bu çalışma, yapay zekâ destekli tasarım süreçleri ve endüstriyel inşaat tekniklerinin konut mimarisindeki etkileşimini, Kaliforniya'nın Batı Oakland bölgesinde geliştirilen “Project Phoenix” üzerinden incelemektedir. Mimarlık disiplini, küresel konut krizi, iklim değişikliği ve dijitalleşme gibi çok katmanlı sorunlara yanıt ararken; yapay zekâ, üretici algoritmalar ve modüler üretim sistemleri tasarım süreçlerinde dönüştürücü bir rol oynamaktadır. Project Phoenix, Autodesk Forma gibi araçlarla entegre edilen yapay zekâ destekli analizlerle tasarlanmış; üretim süreci ise Factory_OS tarafından geliştirilen modüler ve hızlı montaj sistemleriyle gerçekleştirilmiştir. Proje kapsamında, miselyum bazlı biyomalzemeler ve FRP teknolojisiyle üretilen karbon-negatif cephe panelleri sayesinde sürdürülebilirlik düzeyi artırılmış; aynı zamanda yapıların inşa süresi geleneksel yöntemlere kıyasla dramatik biçimde kısaltılmıştır. Bu vaka analizi, Project Phoenix’in mimari çözümleme, üretim teknolojisi, malzeme seçimi, bağlamsal yaklaşım ve sürdürülebilirlik stratejilerini detaylı biçimde inceleyerek, gelecekteki mimarlık uygulamaları için örnek oluşturabilecek çok katmanlı bir değerlendirme sunmayı amaçlamaktadır. Çalışma, dijitalleşen tasarım süreçleri ile mimari üretimin dönüşümüne dair teorik ve uygulamalı bir çerçeve önermektedir.

Keywords

• Yapay zekâ destekli tasarım
• Endüstriyel inşaat
• Sürdürülebilir mimarlık
• Modüler konut sistemleri
• Vaka analizi

Ethical Statement

Bu çalışma, yürütülme biçimi itibarıyla herhangi bir etik ihlal içermemektedir.

References

1. Abd El-Maksoud, N. M., Ahmed, E. A. (2024). Artificial intelligence applications in green architecture. Journal of Fayoum University Faculty of Engineering, Vol. 7, Issue 2, p.317–337. https://doi.org/10.21608/fuje.2024.345049
2. Adetayo, A. J. (2024). Reimagining learning through AI art: The promise of DALL-E and MidJourney for education and libraries. Library Hi Tech News, Vol. 41, Issue 1, p.1–18. https://doi.org/10.1108/LHTN-01-2024-0005
3. Almaz, A. F., El-Agouz, E. A. E., Abdelfatah, M. T., Mohamed, I. R. (2024). The future role of artificial intelligence (AI) design's integration into architectural and interior design education is to improve efficiency, sustainability, and creativity. Department of Architecture Engineering, Horus University; Tanta University; Arab Academy for Science, Technology and Maritime Transport.
4. Avinç, G. M. (2024). The use of text-to-image generation artificial intelligence tools for the production of biophilic design in architecture. Black Sea Journal of Engineering and Science, Vol. 7, Issue 3, p.641–650. https://doi.org/10.34248/bsengineering.1470411
5. Bölek, B., Tutal, O., Özbaşaran, H. (2023). A systematic review on artificial intelligence applications in architecture. Journal of Design for Resilience in Architecture & Planning, Vol. 4, Issue 1, p.91–104. https://doi.org/10.47818/DRArch.2023.v4i1085
6. Buldaç, M. (2024). Use of artificial intelligence tools in the experimental design process: Outcomes of a course model in interior design education. Sanat ve Tasarım Dergisi, Cilt 14, Sayı 2, s.69–91. https://doi.org/10.20488/sanattasarim.1602366
7. Cao, Y., Gao, X., Yin, H., Yu, K., Zhou, D. (2024). Reimagining tradition: A comparative study of artificial intelligence and virtual reality in sustainable architecture education. Sustainability, Vol. 16, Issue 24, p.11135. https://doi.org/10.3390/su162411135
8. Chen, Y., Zhao, M., Liu, H. (2024). User-controlled AI-based architectural prototype production system. Advanced Engineering Informatics, 55, p.101812. https://doi.org/10.1016/j.aei.2023.101812

9. Dorsey, J., Xu, S., Smedresman, G., Rushmeier, H., McMillan, L. (2007). The Mental Canvas: A tool for conceptual architectural design and analysis. 15th Pacific Conference on Computer Graphics and Applications, p.201–210. IEEE. https://doi.org/10.1109/PG.2007.64
10. Harapan, F., Andi, S., Gunagama, A. F. (2021). Artificial intelligence in architectural design. International Journal of Design, Vol. 15, Issue 1, p.1–6.
11. Jin, S., Tu, H., Li, J., Fang, Y., Qu, Z., Xu, F., Liu, K., Lin, Y. (2024). Enhancing architectural education through artificial intelligence: A case study of an AI-assisted architectural programming and design course. Buildings, Vol. 14, Issue 1, p.118. https://doi.org/10.3390/buildings14010118
12. Li, Y., Chen, H., Yu, P., Yang, L. (2025). A review of artificial intelligence applications in architectural design: Energy-saving renovations and adaptive building envelopes. Energies, Vol. 18, Issue 4, p.918. https://doi.org/10.3390/en18040918
13. Lukovich, A. (2023). Advances in AI-based architectural design methods. International Journal of Architectural Computing, Vol. 21, Issue 3, p.215–230.
14. Mammadov, E., Asgarov, A., Mammadova, A. (2025). The role of artificial intelligence in modern computer architecture: From algorithms to hardware optimization. Portuni, Vol. 1, Issue 2, Article 010208. https://doi.org/10.69760/portuni.010208
15. Marşoğlu, Z., Özdemir, Ş. (2025). Yapay zekâ destekli senaryo görselleştirme ve storyboard geliştirme: İç mimarlık stüdyosu örneği. İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt 7, Sayı 1, s.30–39. https://doi.org/10.47769/izufbed.1633621
16. Nie, X. (2024). Exploration of Stable Diffusion in architectural design. Applied Science and Innovative Research, Vol. 8, Issue 3, p.1–12. https://doi.org/10.22158/asir.v8n3p1
17. Park, S., Kim, J. (2024). Interactive use of AI tools in creative poster design generation. International Journal of Design Creativity and Innovation, Vol. 12, Issue 2, p.88–103. https://doi.org/10.1080/21650349.2023.2214097
18. Ploennigs, J., Berger, M. (2023). AI art in architecture. AI in Civil Engineering, Vol. 2, Issue 8. https://doi.org/10.1007/s43503-023-00018-y
19. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I. (2021). Zero-shot text-to-image generation (arXiv:2102.12092). arXiv. https://doi.org/10.48550/arXiv.2102.12092
20. Sharma, M., Singh, A. K., Saini, R. K. (2023). Generative AI models in architectural visualization. Journal of Computational Design, Vol. 3, Issue 4, p.150–170.
21. Sheikh, A. T., Crolla, K. (2023). Architectural education with virtual reality: An exploration of Unreal Engine 5 and Nvidia Omniverse. In Proceedings of eCAADe 41 – Volume 1: Digital Design Reconsidered (pp. 159–168). Building Simplexity Lab, The University of Hong Kong.
22. Smith, S., Jones, R. (2024). Decision support systems based on AI for architectural conceptual design. Computers in Industry, 150, 103945.
23. Softaoğlu, B. (2024). The role of artificial intelligence in architectural heritage conservation. nHeritage, Vol. 7, Issue 81. https://doi.org/10.3390/heritage7010081
24. Vergunova, N. C. (2024). The role of artificial intelligence in modern computer architecture: From algorithms to hardware optimization. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2024.107014
25. Wang, W., Zhang, Q., Huang, Y., Zhang, L. (2023). Architectural façade design with style and structural features using Stable Diffusion model. Computers & Graphics, 113, p. 140–149. https://doi.org/10.1016/j.cag.2023.04.007
26. Wen, M., Liang, D., Ye, H., Tu, H. (2024). Architectural facade design with style and structural features using stable diffusion model. [Makale kabul edilmiş; yayın bilgisi bekleniyor]. Nanjing University of Aeronautics and Astronautics; La Trobe University.
27. Yaşar, I., Arslan Selçuk, S., Alaçam, S. (2025). Use of artificial intelligence and prompt literacy in architectural education. New Design Ideas, Vol. 9, Issue 1, p.248–268. https://doi.org/10.62476/ndi.91.24
28. Yaman, D. G. K. (2025). İç mimarlık eğitiminde erken tasarım aşamalarında yapay zekâ araçlarının kullanımı. Uluslararası İletişim ve Sanat Dergisi, Cilt 6, Sayı 14, s.65–78. https://doi.org/10.5281/zenodo.1234567
29. URL 1. Benjamin, D. (2023). “Autodesk: Autodesk-led collaboration brings AI-powered, climate-friendly solution to affordable housing” https://adsknews.autodesk.com/en/news/ai-powered-sustainable-housing-phoenix/?_gl=1*1fauceo*_gcl_au*MzkxMTE2OTMyLjE3NTM4NzY5MTI.*FPAU*MzkxMTE2OTMyLjE3NTM4NzY5MTI.*_ga*MTk3MjU1ODI0My4xNzUzODc2OTEy*_ga_NZSJ72N6RX*czE3NTM4NzY5MTIkbzEkZzEkdDE3NTM4NzcxMzgkajU0JGwwJGgw 30.05.2025