Transformative Role of Artificial Intelligence in Enhancing Personalized Sustainable Architectural Design
Emphasizing the harmony between environmental goals and personal preferences, the study looks at how sustainable building design relates to human preferences. While sustainable design advances health, resource economy, and energy efficiency, human tastes occasionally center on aesthetics and materials that might be incompatible with sustainability. This study investigates how artificial intelligence (AI) could reconcile these conflicting objectives so that architects may create unique designs while also adhering to environmental standards. Large dataset evaluation capability of AI helps it to identify trends in architectural designs, personal preferences, and environmental needs. Architects can design customized, responsive settings that mix human aesthetic choices with sustainability by applying Training on Architectural Data (TOD) method. Using AI, the study grouped words associated with beauty, proportion, color, and materials across architectural movements to provide context on their perceptions. Moreover, architect-specific prompts produced AI-generated designs that demonstrated how to link architectural trends with personal preferences. This paper underlines how revolutionary AI is in enhancing tailored and sustainable architecture and highlights future opportunities for collaborative collaboration between architects and data scientists to propel invention in human-centered design. The results show how well AI can examine vast amounts of data and produce relevant visual representations matching certain architectural trends and unique designs of individual architects. AI generates fresh design innovation by tying human preferences to architectural trends, therefore enabling architects to mix current aesthetics with cultural relevance and environmental principles.
Authors
- Wael Sheta (ORCID: https://orcid.org/0000-0002-2862-1138)
Institutions
- British University in Dubai (AE)
Publication Details
- Journal
- EPJ Web of Conferences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/epjconf/202534305007
- Primary Topic
- Architecture and Computational Design
- Type
- article
- Field-Weighted Citation Impact
- 0.00