Machine Learning and Safe and Inclusive Architecture for Fragile Users
The contribution presents the first results of a research conducted in the Department of Architecture, Roma Tre University, aimed at testing Machine Learning algorithms for train Neural Networks in learning data from BIM, with the purpose of generating Augmented Reality contents. The objective is to improve the living space's fruition by fragile users. Machine Learning algorithms, in computer-aided design, constitute an innovation in production, as well as an innovation in product, meaning architectural spaces as such. After describing the current research lines, this paper proposes a shared glossary about the terms borrowed from other investigation fields. Finally, it describes the applications in Augmented Reality experimented in the research and the theoretical mechanisms of interaction between these and the Machine Learning algorithms.
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