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Abstract

The surge in generative AI poses a twofold challenge for architecture: crafting specialized algorithms and curating top-tier databases for machine learning. This article presents PUBLICPLAN, a database featuring social housing floor plans from recent competitions. Architecturally curated databases transcend randomness, embodying coherent compilations with recurring patterns. Historical architectural series like Mies Van der Rohe’s courtyard houses, John Hejduk’s Diamond series, and William J. Mitchell’s Palladian Grammar are explored, highlighting the relevance of systematic approaches. Despite digital nuances, both authorial series abstraction and automated learning converge, reshaping architectural authorship paradigms. Architectural databases for AI training tap into collective intelligence, contrasting individual architect authorship. An experiment suggests assessing AI-generated floor plans’ authenticity using PUBLICPLAN, seeking expert input on their human-like quality.

Keywords

Architectural authorship architectural database architectural design computational model design methodology design thinking machine learning

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Database for serialized authorship: The case of publicplan

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