From Clay to Code: Typological and Material Reasoning in AI Interpretations of Iranian Pigeon Towers
This research addresses the problem of AI's limited understanding of architectural reasoning for designers and cultural preservationists, though it is incremental in analyzing existing models.
The study investigated how generative AI systems interpret architectural intelligence in vernacular forms, using Iranian pigeon towers as a case study, and found that AI reliably reproduces geometric patterns but misreads material and climatic reasoning, with reference imagery improving realism but limiting creativity.
This study investigates how generative AI systems interpret the architectural intelligence embedded in vernacular form. Using the Iranian pigeon tower as a case study, the research tests three diffusion models, Midjourney v6, DALL-E 3, and DreamStudio based on Stable Diffusion XL (SDXL), across three prompt stages: referential, adaptive, and speculative. A five-criteria evaluation framework assesses how each system reconstructs typology, materiality, environment, realism, and cultural specificity. Results show that AI reliably reproduces geometric patterns but misreads material and climatic reasoning. Reference imagery improves realism yet limits creativity, while freedom from reference generates inventive but culturally ambiguous outcomes. The findings define a boundary between visual resemblance and architectural reasoning, positioning computational vernacular reasoning as a framework for analyzing how AI perceives, distorts, and reimagines traditional design intelligence.