Assem, A. (2025). Bridging Physical and Digital Realms: An Innovative AI-Driven Methodology for Architectural Conceptualization. JES. Journal of Engineering Sciences, 53(1), 59-79. doi: 10.21608/jesaun.2024.331481.1378
Ayman Assem. "Bridging Physical and Digital Realms: An Innovative AI-Driven Methodology for Architectural Conceptualization". JES. Journal of Engineering Sciences, 53, 1, 2025, 59-79. doi: 10.21608/jesaun.2024.331481.1378
Assem, A. (2025). 'Bridging Physical and Digital Realms: An Innovative AI-Driven Methodology for Architectural Conceptualization', JES. Journal of Engineering Sciences, 53(1), pp. 59-79. doi: 10.21608/jesaun.2024.331481.1378
Assem, A. Bridging Physical and Digital Realms: An Innovative AI-Driven Methodology for Architectural Conceptualization. JES. Journal of Engineering Sciences, 2025; 53(1): 59-79. doi: 10.21608/jesaun.2024.331481.1378
Bridging Physical and Digital Realms: An Innovative AI-Driven Methodology for Architectural Conceptualization
Dept. of Architectural. Engineering, Ain Shams University, Cairo, Egypt
Abstract
This research presents a novel AI-driven approach for architectural conceptualization that effectively merges physical and digital design domains. The effectiveness of the suggested framework was thoroughly assessed via a design competition that tasked participants with developing an innovative faculty gate design. The approach consists of a seven-stage framework: site analysis and documentation, initial documentation, conceptual development, physical model creation, model documentation, AI design generation, and AI design enhancement. This framework creates a seamless integration of practical design methodologies and sophisticated AI technologies, including stable diffusion and diverse web UIs. Participating teams exhibited the framework's versatility by utilizing several design tactics, including analogical and metaphorical design methods. The abstracted white paper models served as crucial controllers for AI-generated designs, enabling a smooth transition from physical ideation to digital reality. The final phase utilized AI methodologies like inpainting, outpainting, and upscaling to improve the generated designs. The competition results highlighted the framework's effectiveness, with participants demonstrating a range of innovative approaches and successful integration of physical modeling with AI-enhanced design visualization. This methodology combines physical modeling with AI-driven design tools, enabling architects to investigate a wider range of creative options while maintaining a link to conventional design methods. These results indicate that the seven-stage AI-integrated design framework could significantly revolutionize architectural conceptualization.
[1] A. F. Almaz, E. A. E. A. El-Agouz, M. T. Abdelfatah, and I. R. Mohamed, “The Future Role of Artificial Intelligence (AI) Design’s Integration into Architectural and Interior Design Education is to Improve Efficiency, Sustainability, and Creativity,” Civil Engineering and Architecture, vol. 12, no. 3, pp. 1749–1772, May 2024, doi: 10.13189/CEA.2024.120336.
[2] S. Chaillou and S. Chaillou, “ArchiGAN: Artificial Intelligence x Architecture,” Architectural Intelligence, pp. 117–127, 2020, doi: 10.1007/978-981-15-6568-7_8.
[3] M. Sari, M. A. Berawi, T. Y. Zagloel, and R. W. Triadji, “Machine learning model for green building design prediction,” IAES International Journal of Artificial Intelligence, vol. 11, no. 4, pp. 1525–1534, Dec. 2022, doi: 10.11591/IJAI.V11.I4.PP1525-1534.
[4] N. M.Matter and N. G.Gado, “Artificial Intelligence in Architecture: Integration into Architectural Design Process,” Engineering Research Journal, vol. 181, no. 0, pp. 1–16, Mar. 2024, doi: 10.21608/ERJ.2024.344313.
[5] I. Caetano, L. Santos, and A. Leitão, “Computational design in architecture: Defining parametric, generative, and algorithmic design,” Frontiers of Architectural Research, vol. 9, no. 2, pp. 287–300, Jun. 2020, doi: 10.1016/J.FOAR.2019.12.008.
[6] J. Cudzik and K. Radziszewski, “Artificial Intelligence Aided Architectural Design,” Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 77-84, vol. 1, pp. 77–84, 2018, doi: 10.52842/CONF.ECAADE.2018.1.077.
[7] A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, Jun. 2020, doi: 10.1016/J.INFFUS.2019.12.012.
[8] G. D. Hager et al., “Artificial Intelligence for Social Good,” Jan. 2019, Accessed: Oct. 24, 2024. [Online]. Available: https://arxiv.org/abs/1901.05406v1
[9] R. Oxman, “Thinking difference: Theories and models of parametric design thinking,” Des Stud, vol. 52, pp. 4–39, Sep. 2017, doi: 10.1016/J.DESTUD.2017.06.001.
[10] A. Kaplan and M. Haenlein, “Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence,” Bus Horiz, vol. 62, no. 1, pp. 15–25, Jan. 2019, doi: 10.1016/J.BUSHOR.2018.08.004.
[11] M. L. Castro Pena, A. Carballal, N. Rodríguez-Fernández, I. Santos, and J. Romero, “Artificial intelligence applied to conceptual design. A review of its use in architecture,” Autom Constr, vol. 124, p. 103550, Apr. 2021, doi: 10.1016/J.AUTCON.2021.103550.
[12] NEIL. LEACH and P. F. . . Yuan, “COMPUTATIONAL DESIGN,” p. 303, 2018, Accessed: Oct. 24, 2024. [Online]. Available: https://books.google.com/books/about/Computational_Design.html?id=pD1nswEACAAJ
[13] A. Wang, J. Dong, L.-H. Lee, J. Shen, and P. Hui, “Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning,” Apr. 2023, Accessed: Dec. 08, 2024. [Online]. Available: https://arxiv.org/abs/2305.00510v4
[14] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Computer Vision and Pattern Recognition, vol. 2022-June, pp. 10674–10685, 2021, doi: 10.1109/CVPR52688.2022.01042.
[15] Y. Cao, A. Abdul Aziz, and W. N. R. Mohd Arshard, “Stable diffusion in architectural design: Closing doors or opening new horizons?” International Journal of Architectural Computing, 2024, doi: 10.1177/14780771241270257.
[16] S. K. R. Maryada et al., “Applying a novel two-stage deep-learning model to improve accuracy in detecting retinal fundus images,” Medical Imaging, p. 21, Apr. 2022, doi: 10.1117/12.2611565.
[17] L. Zhang, A. Rao, and M. Agrawala, “Adding Conditional Control to Text-to-Image Diffusion Models,” Proceedings of the IEEE International Conference on Computer Vision, pp. 3813–3824, Feb. 2023, doi: 10.1109/ICCV51070.2023.00355.
[18] T. Berčič, M. Bohanec, and L. Ažman Momirski, “Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions,” Smart Cities, vol. 7, no. 2, pp. 786–805, Apr. 2024, doi: 10.3390/SMARTCITIES7020033/S1.
[19] M. Rönn, “Architectural quality in competitions. A dialogue based assessment of design proposals,” FormAkademisk, vol. 4, no. 1, Jul. 2011, doi: 10.7577/FORMAKADEMISK.130.
View on SCiNiTO
Top