Tartışma
BibTex RIS Kaynak Göster

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

Yıl 2025, , 191 - 210, 31.03.2025

Özlem Gök Tokgöz , Mehmet Ali Altın

https://doi.org/10.53710/jcode.1448847

Öz

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.

Anahtar Kelimeler

GAN Architectural Plan Generation Quality in Architectural Plan Artificial Intelligence

Kaynakça

  • Akçan, M. Z. (2022). Yapay zekâ algoritmalarının mimari şematik plan oluşturmak için kullanımı. (Thesis no: 771891) [Master Thesis, Mimar Sinan Fine Art University]. https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=kIrIdtdJ31bRgjb6fHvMUeKHzPHwk4E_1TYrnsGJ7i8vNE6sv9M-SjFtInsax_kL
  • Akdoğan, M., & Balaban, Ö. (2022). Plan generation with generative adversarial networks: Haeckel’s Drawings to Palladian plans. Journal of Computational Design 3(1), 135-154. https://doi.org/10.53710/jcode.1064225
  • Akın, T. (2006). R.M. Pirsig’in nitelik düşüncesi ve mimarlık. (Thesis no: 180463) [Master Thesis, İstanbul Technical University].
  • As, I., & Basu, P. (Eds.). (2021). The Routledge companion to artificial intelligence in architecture. Routledge. https://doi.org/10.4324/9780367824259
  • Chaillou, S. (2019, July 17). ArchiGAN: a generative stack for apartment building design. NVIDIA Corporation. https://developer.nvidia.com/blog/archigan-generative-stack-apartment-building-design/
  • Chaillou, S. (2021). AI and architecture an experimental perspective. In I. As & P. Basu (Eds.), The Routledge companion to artificial intelligence in architecture (1st ed.). Routledge. https://doi.org/10.4324/9780367824259
  • Chaillou, S. (2022). Artificial intelligence and architecture:From research to practice. Birkhäuser. https://doi.org/10.1515/9783035624045
  • Deprez, L., Verstraeten, R., & Pauwels, P. (2023). Data-based generation of residential floorplans using neural networks. Design Computing and Cognition’22 (pp. 321–339). Springer International Publishing. https://doi.org/10.1007/978-3-031-20418-0_20
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. 27th International Conference on Neural Information Processing Systems, 2672–2680. https://doi.org/10.48550/arXiv.1406.2661
  • Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1125–1134. https://doi.org/10.1109/CVPR.2017.632
  • Karadağ, İ., Güzelci, O. Z., & Alaçam, S. (2022). Edu-ai: a twofold machine learning model to support classroom layout generation. Construction Innovation, 23(4), 898-914. https://doi.org/10.1108/ci-02-2022-0034
  • Kul, F. (2019). Günümüz ve yakın geçmişte mimarlık ediminde nitelik sorunsalı. (Thesis no: 601080) [Master Thesis, İstanbul Technical University].
  • Liu, Y., Fang, C., Yang, Z., Wang, X., Zhou, Z., Deng, Q., & Liang, L. (2022). Exploration on machine mearning layout generation of chinese private garden in southern Yangtze. In Proceedings of the 2021 DigitalFUTURES (pp. 35–44). Springer Singapore. https://doi.org/10.1007/978-981-16-5983-6_4
  • Liu, Y., Luo, Y., Deng, Q., & Zhou, X. (2021). Exploration of campus layout based on generative adversarial network. In Proceedings of the 2020 DigitalFUTURES (pp. 169–178). Springer Singapore. https://doi.org/10.1007/978-981-33-4400-6_16
  • Nauata, N., Hosseini, S., Chang, K.-H., Chu, H., Cheng, C.-Y., & Furukawa, Y. (2021). House-GAN++: Generative adversarial layout refinement network towards Intelligent Computational Agent for Professional Architects. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13627–13636. https://doi.org/10.1109/CVPR46437.2021.01342
  • Nelson, C. (2017). Managing quality in architecture. Routledge. https://doi.org/10.4324/9781315272382
  • Newton, D. (2019). Deep generative learning for the generation and analysis of architectural plans with small datasets. In Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1) (pp. 21–28). https://doi.org/10.5151/proceedings-ecaadesigradi2019_135
  • Özerol, G., & Arslan Selçuk, S. (2022). Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020. International Journal of Architectural Computing, 0–19. https://doi.org/10.1177/14780771221100102
  • Ozman, G. Ö., & Selçuk, S. A. (2023). Generating mass housing plans through GANs - A case in TOKI, Turkey. Architecture and Planning Journal (APJ), 28(3). https://doi.org/10.54729/2789-8547.1197
  • Park, S.-W., Ko, J.-S., Huh, J.-H., & Kim, J.-C. (2021). Review on generative adversarial networks: Focusing on computer vision and its applications. Electronics, 10(10), 1216. https://doi.org/10.3390/electronics10101216
  • Shmelkov, K., Schmid, C., & Alahari, K. (2018). How good is my GAN? In Proceedings of the European Conference on Computer Vision (ECCV), 213–229. https://doi.org/10.48550/arXiv.1807.09499
  • Tian, R. (2021). Suggestive site planning with conditional GAN and urban GIS data. In Proceedings of the 2020 DigitalFUTURES (pp. 103–113). Springer Singapore. https://doi.org/10.1007/978-981-33-4400-6_10
  • Uzun, C. (2020a). GAN ile mimari plan üretimlerinin değerlendirilmesi üzerine bir durum çalışması. JCoDe: Journal of Computational Design, 1(3), 167–182. https://dergipark.org.tr/tr/download/article-file/1266251
  • Uzun, C. (2020b). Yapay zeka ve mimarlık etkileşimi üzerine bir çalışma; üretken çekişmeli ağ algoritması ile otonom mimari plan üretimi ve değerlendirilmesi [Ph.D Thesis, Istanbul Technical University]. https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=wf-FPgY-5qjHEzEoOgvMs2-HwOTOkaMt1-NTZbF-pr-K68Q-6HOUSJ82GBZaVsLD
  • Uzun, C., Çolakoğlu, M. B., & Inceoğlu, A. (2020). GAN as a generative architectural plan layout tool: A case study for training DCGAN with Palladian Plans and evaluation of DCGAN outputs. A/Z : ITU Journal of Faculty of Architecture, 17(2), 185–198. https://doi.org/10.5505/itujfa.2020.54037
  • Wu, W., Fu, X.-M., Tang, R., Wang, Y., Qi, Y.-H., & Liu, L. (2019). Data-driven interior plan generation for residential buildings. ACM Trans. Graph., 38(6). https://doi.org/10.1145/3355089.3356556
  • Wu, X., Xu, K., & Hall, P. (2017). A survey of image synthesis and editing with generative adversarial networks. Tsinghua Science and Technology, 22(6), 660–674. https://doi.org/10.23919/TST.2017.8195348
  • Ye, X., Du, J., & Ye, Y. (2022). MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks. Environment and Planning B: Urban Analytics and City Science, 49(3), 794–814. https://doi.org/10.1177/23998083211023516
  • Zheng, H., & Huang, W. (2018). Architectural drawings recognition and generation through machine learning. In Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA). https://doi.org/10.52842/conf.acadia.2018.156

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

Yıl 2025, , 191 - 210, 31.03.2025

Özlem Gök Tokgöz , Mehmet Ali Altın

https://doi.org/10.53710/jcode.1448847

Öz

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.

Anahtar Kelimeler

Yapay Zekâ Mimari Plan Üretimi Mimari Planda Nitelik Çekişmeli Üretken Ağlar

Kaynakça

  • Akçan, M. Z. (2022). Yapay zekâ algoritmalarının mimari şematik plan oluşturmak için kullanımı. (Thesis no: 771891) [Master Thesis, Mimar Sinan Fine Art University]. https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=kIrIdtdJ31bRgjb6fHvMUeKHzPHwk4E_1TYrnsGJ7i8vNE6sv9M-SjFtInsax_kL
  • Akdoğan, M., & Balaban, Ö. (2022). Plan generation with generative adversarial networks: Haeckel’s Drawings to Palladian plans. Journal of Computational Design 3(1), 135-154. https://doi.org/10.53710/jcode.1064225
  • Akın, T. (2006). R.M. Pirsig’in nitelik düşüncesi ve mimarlık. (Thesis no: 180463) [Master Thesis, İstanbul Technical University].
  • As, I., & Basu, P. (Eds.). (2021). The Routledge companion to artificial intelligence in architecture. Routledge. https://doi.org/10.4324/9780367824259
  • Chaillou, S. (2019, July 17). ArchiGAN: a generative stack for apartment building design. NVIDIA Corporation. https://developer.nvidia.com/blog/archigan-generative-stack-apartment-building-design/
  • Chaillou, S. (2021). AI and architecture an experimental perspective. In I. As & P. Basu (Eds.), The Routledge companion to artificial intelligence in architecture (1st ed.). Routledge. https://doi.org/10.4324/9780367824259
  • Chaillou, S. (2022). Artificial intelligence and architecture:From research to practice. Birkhäuser. https://doi.org/10.1515/9783035624045
  • Deprez, L., Verstraeten, R., & Pauwels, P. (2023). Data-based generation of residential floorplans using neural networks. Design Computing and Cognition’22 (pp. 321–339). Springer International Publishing. https://doi.org/10.1007/978-3-031-20418-0_20
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. 27th International Conference on Neural Information Processing Systems, 2672–2680. https://doi.org/10.48550/arXiv.1406.2661
  • Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1125–1134. https://doi.org/10.1109/CVPR.2017.632
  • Karadağ, İ., Güzelci, O. Z., & Alaçam, S. (2022). Edu-ai: a twofold machine learning model to support classroom layout generation. Construction Innovation, 23(4), 898-914. https://doi.org/10.1108/ci-02-2022-0034
  • Kul, F. (2019). Günümüz ve yakın geçmişte mimarlık ediminde nitelik sorunsalı. (Thesis no: 601080) [Master Thesis, İstanbul Technical University].
  • Liu, Y., Fang, C., Yang, Z., Wang, X., Zhou, Z., Deng, Q., & Liang, L. (2022). Exploration on machine mearning layout generation of chinese private garden in southern Yangtze. In Proceedings of the 2021 DigitalFUTURES (pp. 35–44). Springer Singapore. https://doi.org/10.1007/978-981-16-5983-6_4
  • Liu, Y., Luo, Y., Deng, Q., & Zhou, X. (2021). Exploration of campus layout based on generative adversarial network. In Proceedings of the 2020 DigitalFUTURES (pp. 169–178). Springer Singapore. https://doi.org/10.1007/978-981-33-4400-6_16
  • Nauata, N., Hosseini, S., Chang, K.-H., Chu, H., Cheng, C.-Y., & Furukawa, Y. (2021). House-GAN++: Generative adversarial layout refinement network towards Intelligent Computational Agent for Professional Architects. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13627–13636. https://doi.org/10.1109/CVPR46437.2021.01342
  • Nelson, C. (2017). Managing quality in architecture. Routledge. https://doi.org/10.4324/9781315272382
  • Newton, D. (2019). Deep generative learning for the generation and analysis of architectural plans with small datasets. In Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1) (pp. 21–28). https://doi.org/10.5151/proceedings-ecaadesigradi2019_135
  • Özerol, G., & Arslan Selçuk, S. (2022). Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020. International Journal of Architectural Computing, 0–19. https://doi.org/10.1177/14780771221100102
  • Ozman, G. Ö., & Selçuk, S. A. (2023). Generating mass housing plans through GANs - A case in TOKI, Turkey. Architecture and Planning Journal (APJ), 28(3). https://doi.org/10.54729/2789-8547.1197
  • Park, S.-W., Ko, J.-S., Huh, J.-H., & Kim, J.-C. (2021). Review on generative adversarial networks: Focusing on computer vision and its applications. Electronics, 10(10), 1216. https://doi.org/10.3390/electronics10101216
  • Shmelkov, K., Schmid, C., & Alahari, K. (2018). How good is my GAN? In Proceedings of the European Conference on Computer Vision (ECCV), 213–229. https://doi.org/10.48550/arXiv.1807.09499
  • Tian, R. (2021). Suggestive site planning with conditional GAN and urban GIS data. In Proceedings of the 2020 DigitalFUTURES (pp. 103–113). Springer Singapore. https://doi.org/10.1007/978-981-33-4400-6_10
  • Uzun, C. (2020a). GAN ile mimari plan üretimlerinin değerlendirilmesi üzerine bir durum çalışması. JCoDe: Journal of Computational Design, 1(3), 167–182. https://dergipark.org.tr/tr/download/article-file/1266251
  • Uzun, C. (2020b). Yapay zeka ve mimarlık etkileşimi üzerine bir çalışma; üretken çekişmeli ağ algoritması ile otonom mimari plan üretimi ve değerlendirilmesi [Ph.D Thesis, Istanbul Technical University]. https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=wf-FPgY-5qjHEzEoOgvMs2-HwOTOkaMt1-NTZbF-pr-K68Q-6HOUSJ82GBZaVsLD
  • Uzun, C., Çolakoğlu, M. B., & Inceoğlu, A. (2020). GAN as a generative architectural plan layout tool: A case study for training DCGAN with Palladian Plans and evaluation of DCGAN outputs. A/Z : ITU Journal of Faculty of Architecture, 17(2), 185–198. https://doi.org/10.5505/itujfa.2020.54037
  • Wu, W., Fu, X.-M., Tang, R., Wang, Y., Qi, Y.-H., & Liu, L. (2019). Data-driven interior plan generation for residential buildings. ACM Trans. Graph., 38(6). https://doi.org/10.1145/3355089.3356556
  • Wu, X., Xu, K., & Hall, P. (2017). A survey of image synthesis and editing with generative adversarial networks. Tsinghua Science and Technology, 22(6), 660–674. https://doi.org/10.23919/TST.2017.8195348
  • Ye, X., Du, J., & Ye, Y. (2022). MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks. Environment and Planning B: Urban Analytics and City Science, 49(3), 794–814. https://doi.org/10.1177/23998083211023516
  • Zheng, H., & Huang, W. (2018). Architectural drawings recognition and generation through machine learning. In Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA). https://doi.org/10.52842/conf.acadia.2018.156
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Planlama ve Karar Verme
Bölüm Tartışma
Yazarlar

Özlem Gök Tokgöz 0000-0002-1702-0126

Mehmet Ali Altın 0000-0001-8992-7088

Erken Görünüm Tarihi 28 Mart 2025
Yayımlanma Tarihi 31 Mart 2025
Gönderilme Tarihi 9 Mart 2024
Kabul Tarihi 30 Eylül 2024
Yayımlandığı Sayı Yıl 2025

Kaynak Göster

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

88x31.png

JCoDe makaleleri "Creative Commons Attribution-NonCommercial 4.0 International License" altında yayınlanmaktadır.