The integration of artificial intelligence (AI) within the built environment is driving a paradigm shift—enhancing innovation, streamlining processes, and promoting sustainability—while simultaneously redefining conventional design methodologies. This study explores the benefits and limitations of three AI-driven applications in the context of urban design: PlanFinder, TestFit, and Luma.
PlanFinder demonstrates exceptional capability in generating diverse design alternatives within seconds, thereby accelerating workflows for both novice and experienced designers. TestFit supports urban design by producing site-specific solutions that align with cost parameters and regulatory requirements, empowering planners and developers to make data-driven decisions. Meanwhile, Luma enhances visualization by transforming simple architectural models into highly realistic 3D animations, significantly improving communication and presentation quality.
While these applications offer substantial advantages—including time efficiency, cost reduction, and expanded creative potential—they also pose notable challenges. These include dependence on accurate input data, limited flexibility for manual modifications, and integration difficulties with complex or unconventional project demands. Furthermore, the adoption of AI raises ethical considerations, particularly regarding its influence on employment within the design and urban design sectors.
This research employs a structured methodology using real-world case studies to evaluate three AI-powered design tools—PlanFinder, TestFit, and Luma—based on their accuracy, speed, usability, flexibility, output quality, and software integration, and provides critical insights for architects, urban planners, and educators aiming to adopt AI tools in their workflows. It serves as a practical guide to understanding the capabilities, constraints, and future implications of artificial intelligence in the built environment. |
- Ko, J., Ennemoser, B., Yoo, W., Yan, W., & Clayton, M. J. (2023). Architectural spatial layout planning using artificial intelligence. Automation in Construction. Elsevier. https://www.sciencedirect.com/science/article/pii/S0926580523002790
- Jaruga-Rozdolska, A. (2022). Artificial intelligence as part of future practices in the architect’s work: MidJourney generative tool as part of a process of creating an architectural form. Architectus. https://bibliotekanauki.pl/articles/2174415.pdf
- Yussuf, R. O., & Asfour, O. S. (2024). Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview. Energy and Buildings. Elsevier. https://www.sciencedirect.com/science/article/pii/S0378778824000197
- Almaz, A. F., El-Agouz, E. A. E., Abdelfatah, M. T., & Mohamed, I. R. (2024). The future role of AI design integration in architectural and interior design education: Improving efficiency, sustainability, and creativity. Sustainability and Creativity. ResearchGate. https://www.researchgate.net/publication/379665362_The_Future_Role_of_AI_Design_Integration
- Liang, C. J., Le, T. H., Ham, Y., Mantha, B. R. K., Cheng, M. H., & Lin, J. J. (2024). Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Automation in Construction. Elsevier. https://www.sciencedirect.com/science/article/pii/S0926580524001055
- Jiang, F., Ma, J., Webster, C., Chiaradia, A., & Zhang, Y. (2024). Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Elsevier. https://www.sciencedirect.com/science/article/pii/S0305900623000569
- Rane, N., Choudhary, S., & Rane, J. (2023). Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in architectural design and engineering: Applications, framework, and challenges. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4645595
- Cudzik, J., Nyka, L., & Szczepański, J. (2024). Artificial intelligence in architectural education: Green campus development research. Global Journal of Engineering Education. https://mostwiedzy.pl/pl/publication/download/1/artificial-intelligence-in-architectural-education
- Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523
- He, W., & Chen, M. (2024). Advancing urban life: A systematic review of emerging applications and artificial intelligence in urban design and planning. Buildings, 14(3), 835. https://www.mdpi.com/2075-5309/14/3/835
- Ertel, W. (2024). Introduction to artificial intelligence. Springer Nature. https://books.google.com/books?hl=en&id=O7kfEQAAQBAJ
- Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K. M. I., Alonso-Moral, J. M., & Confalonieri, R. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence. Information Fusion. https://www.sciencedirect.com/science/article/pii/S1566253523001148
- Schwendicke, F., Arsiwala-Scheppach, L. T., & Krois, J. (2023). Artificial intelligence: What it is and what it can do for dentists. Dental Update, 50(4), 245–249. https://doi.org/10.12968/denu.2023.50.4.245
- Plan Finder. (n.d.). AI-based architectural design generator. https://www.planfinder.xyz/
- Test Fit. (n.d.). Real estate feasibility platform. https://www.testfit.io/
|
View on SCiNiTO
