ANALYZING AESTHETIC AND FUNCTIONAL SOLUTIONS FOR BUILDING FACADES USING ARTIFICIAL INTELLIGENCE

Abstract

The facade shapes a building’s architectural expression as well as its energy performance, comfort, and durability. This article analyzes how artificial intelligence (AI) can support the design and evaluation of facade solutions, reviewing machine learning, generative design, and computer vision methods applied to facade composition, material selection, daylight and thermal performance, and defect detection. A comparative analysis of AI-assisted and conventional approaches shows that generative models and neural networks can quickly produce and rank many facade alternatives, enabling multi-criteria optimization of shading, glazing ratio, and materials while preserving architectural character. Computer vision combined with UAV data also improves facade condition assessment. Limitations include dependence on data quality and limited model interpretability. The study concludes that AI is most effective as a decision-support tool that complements the architect’s judgment in creating sustainable, functional, and visually coherent facades.