TOWARDS INTELLIGENT ARCHITECTURE: A FLOW-CHART FORESIGHT ON AI-DRIVEN DESIGN AND OPTIMIZATION

Year 2024, Volume: 1 Issue: 2, 49 - 59, 30.09.2024

Erdem Yıldırım

Abstract

Architectural design is being revolutionized by Artificial Intelligence (AI), which provides new opportunities for creativity, optimization and continuous improvement. Incorporating feedback loops from the generation of conceptual ideas to the optimization of design alternatives, this paper presents a forward-looking flowchart that illustrates the integration of AI into various inclusive stages of the architectural design process. The continuous integration of user input through these ‘feedback loops’ is essential for the refinement and enhancement of design outcomes, thereby enhancing the process's adaptability and responsiveness to social, cultural, and behavioral factors. By incorporating iterative feedback mechanisms at each stage, AI-driven design tools allow architects to create solutions that are not only in compliance with technical and performance standards, but also more closely aligned with user needs and expectations. In this study, the potential of AI to improve ‘user-centered design processes’ is emphasized through the development of a dynamic, feedback-driven workflow that encourages adaptability. Through a literature review, the paper investigates the technical, ethical, and practical implications of inclusive AI integration and its potential to transform the future of architecture.

Keywords

Machine Learning, Artificial Intelligence, Architectural Design, Design Optimization, Feedback Loops

AKILLI MİMARLIĞA DOĞRU: YAPAY ZEKA YÖNLENDİRMELİ GERİBİLDİRİM DÖNGÜLERİ ÜZERİNE BİR AKIŞ ŞEMASI ÖNGÖRÜSÜ

Abstract

Yapay Zeka, mimari tasarım alanını dönüştürme potansiyeline sahip olup, yaratıcılık, optimizasyon ve sürekli iyileştirme için yeni fırsatlar sunmaktadır. Bu makale, yapay zekanın mimari tasarım sürecinin çeşitli aşamalarına entegrasyonunu, kavramsal fikir üretiminden enerji verimliliği, malzeme kullanımı ve maliyet bazında tasarım alternatiflerinin optimizasyonuna kadar geniş bir perspektiften ele alan ileriye dönük bir akış şeması sunmaktadır. Yapay zeka destekli araçlar, tasarım iş akışlarını hızlandırma, yaratıcı olanakları genişletme ve mimari çözümlerin hem yenilikçi hem de sürdürülebilir olmasını sağlama potansiyeline sahiptir. Bu makalenin temel odak noktası, geri besleme döngülerinin tasarım modellerinin iyileştirilmesi ve geliştirilmesindeki rolüdür; yapay zeka, kullanıcı geri bildirimlerinin kesintisiz bir şekilde tasarım sonuçlarını iteratif olarak iyileştirmek üzere dahil edilmesini mümkün kılmaktadır. Literatür incelemesi ve açıklayıcı örnekler aracılığıyla bu makale, yapay zekanın mimarinin geleceğini şekillendirmedeki dönüştürücü potansiyelini araştırırken, uygulama sürecinde karşılaşılan teknik, etik ve pratik zorlukları da ele almaktadır. Makale, gelecekte yapay zekanın mimari uygulamalara entegrasyonu konusunda bir akış şeması önerisi ile sonuçlanmakta olup, geri besleme döngüleri aracılığıyla yapay zeka yetenekleri ile insan yaratıcılığı ve denetimi arasındaki dengenin önemini vurgulamaktadır.

Keywords

Makine Öğrenimi, Yapay Zeka, Mimari Tasarım, Tasarım Optimizasyonu, Geri Besleme Döngüleri

References

• Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M. and Vanoli, G.P. (2017), “CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building”, Energy and Buildings, Elsevier Ltd, Vol. 146, pp. 200–219, doi: 10.1016/j.enbuild.2017.04.069.
• Caldas, L. (2008), “Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system”, Advanced Engineering Informatics, Vol. 22 No. 1, pp. 59–70, doi: 10.1016/j.aei.2007.08.012.
• Castro Pena, M.L., Carballal, A., Rodríguez-Fernández, N., Santos, I. and Romero, J. (2021), “Artificial intelligence applied to conceptual design. A review of its use in architecture”, Automation in Construction, Vol. 124, doi: 10.1016/j.autcon.2021.103550.
• Chen, L., Wang, P., Dong, H., Shi, F., Han, J., Guo, Y., Childs, P.R.N., et al. (2019), “An artificial intelligence based data-driven approach for design ideation”, Journal of Visual Communication and Image Representation, Academic Press Inc., Vol. 61, pp. 10–22, doi: 10.1016/j.jvcir.2019.02.009.
• Chew, I.M., Wong, F., Bono, A., Nandong, J. and Wong, K.I. (2019), “Optimized Computational Analysis of Feedforward and Feedback Control Scheme using Genetic Algorithm Techniques”, IOP Conference Series: Materials Science and Engineering, Vol. 495, Institute of Physics Publishing, Beijing, p. 495, doi: 10.1088/1757-899X/495/1/012020.
• Delgarm, N., Sajadi, B. and Delgarm, S. (2016), “Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC)”, Energy and Buildings, Elsevier Ltd, Vol. 131, pp. 42–53, doi: 10.1016/j.enbuild.2016.09.003.
• Fesanghary, M., Asadi, S. and Geem, Z.W. (2012), “Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm”, Building and Environment, Vol. 49 No. 1, pp. 245–250, doi: 10.1016/j.buildenv.2011.09.030.
• Goodman, E.P., Powles, J., Lorenzo, J. Di, Ferrante, G., Kramer, A., Mattern, S., Mcdonald, S., et al. (2017), “Urbanism Under Google: Lessons from Sidewalk Toronto”, FordhamLawReview, Vol. 88, pp. 457–498.
• Van der Hoeven, F. (2016), “The powerless starchitect: How Zaha Hadid became the first person working on the Al-Wakrah stadium that actually did die”, Project Baikal, Vol. 13, p. 9, doi: 10.7480/projectbaikal.47-48.971.
• Kochmar, E., Vu, D. Do, Belfer, R., Gupta, V., Serban, I.V. and Pineau, J. (2020), “Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System”.
• Lee, J., Cho, W., Kang, D. and Lee, J. (2023), “Simplified Methods for Generative Design That Combine Evaluation Techniques for Automated Conceptual Building Design”, Applied Sciences (Switzerland), Multidisciplinary Digital Publishing Institute (MDPI), Vol. 13 No. 23, doi: 10.3390/app132312856.
• Mannan, A.V. and Islam, M.S. (2023), “Exploring Uncharted Architectural Territories through Generative Adversarial Networks with Human Collaboration”, 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., İstanbul, pp. 1–6, doi: 10.1109/HORA58378.2023.10156712.
• McKnight, M. (2017), “Generative Design: What it is? How is it being used? Why it’s a game changer”, KnE Engineering, Vol. 2, Knowledge E, p. 176, doi: 10.18502/keg.v2i2.612.
• Mehmood, M.U., Chun, D., Zeeshan, Han, H., Jeon, G. and Chen, K. (2019), “A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment”, Energy and Buildings, Elsevier B.V., Vol. 202, p. 109383, doi: 10.1016/j.enbuild.2019.109383.
• Netzer, E. and Geva, A.B. (2020), “Human-in-the-loop active learning via brain computer interface”, Annals of Mathematics and Artificial Intelligence, Springer Science and Business Media Deutschland GmbH, Vol. 88 No. 11–12, pp. 1191–1205, doi: 10.1007/s10472-020-09689-0.
• Oral, E., Chawla, R., Wijkstra, M., Mahyar, N. and Dimara, E. (2023), “From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making”, IEEE Transactions on Visualization and Computer Graphics, Vol. 30, pp. 359–369, doi: 10.1109/TVCG.2023.3326593.
• Paananen, V., Oppenlaender, J. and Visuri, A. (2023), “Using Text-to-Image Generation for Architectural Design Ideation”, pp. 1–14.
• Petráková, L. and Šimkovič, V. (2023), “Architectural alchemy: Leveraging Artificial Intelligence for inspired design – a comprehensive study of creativity, control, and collaboration”, Architecture Papers of the Faculty of Architecture and Design STU, Walter de Gruyter GmbH, Vol. 28 No. 4, pp. 3–14, doi: 10.2478/alfa-2023-0020.
• Quan, S.J., Park, J., Economou, A. and Lee, S. (2019), “Artificial intelligence-aided design: Smart Design for sustainable city development”, Environment and Planning B: Urban Analytics and City Science, SAGE Publications Ltd, Vol. 46 No. 8, pp. 1581–1599, doi: 10.1177/2399808319867946.
• Enjellina, Vilgia Putri Beyan, E., Gisela Cinintya Rossy A. (2023), “A Review of AI Image Generator: Influences, Challenges, and Future Prospects for Architectural Field”, Journal of Artificial Intelligence in Architecture, Vol. 2 No. 1, pp. 53–65
• Wu, W., Fu, X.M., Tang, R., Wang, Y., Qi, Y.H. and Liu, L. (2019), “Data-driven interior plan generation for residential buildings”, ACM Transactions on Graphics, Association for Computing Machinery, Vol. 234 No. 2, doi: 10.1145/3355089.3356556.
• Yu, H., Yang, W. and Li, Q. (2019), “Multi-objective optimization of building’s life cycle performance in early design stages”, IOP Conference Series: Earth and Environmental Science, Vol. 323, Institute of Physics Publishing, Sapporo, p. 323, doi: 10.1088/1755-1315/323/1/012116.
• Zhang, J., Liu, N. and Wang, S. (2021), “Generative design and performance optimization of residential buildings based on parametric algorithm”, Energy and Buildings, Elsevier Ltd, Vol. 244 No. 111033, doi: 10.1016/j.enbuild.2021.111033.