Augmented Intelligence for Architectural Design with Conditional Autoencoders: Semiramis Case Study
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Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We present a design approach that uses machine learning to enhance architect’s design experience. Nowadays, architects and engineers use software for parametric design to generate, simulate, and evaluate multiple design instances. In this paper, we propose a conditional autoencoder that reverses the parametric modelling process and instead allows architects to define the desired properties in their designs and obtain multiple predictions of designs that fulfil them. The results found by the encoder can oftentimes go beyond what the user expected and thus augment human’s understanding of the design task and stimulate design exploration. Our tool also allows the architect to under-define the desired properties to give additional flexibility to finding interesting solutions. We specifically illustrate this tool for architectural design of a multi-storey structure that has been built in 2022 in Zug, Switzerland. Show more
Publication status
publishedExternal links
Editor
Book title
Towards Radical RegenerationPages / Article No.
Publisher
SpringerEvent
Subject
Architectural Design; Machine Learning; Conditional Autoencoders; Generative Design; Timber ArchitectureOrganisational unit
03709 - Kohler, Matthias / Kohler, Matthias
02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)
03708 - Gramazio, Fabio / Gramazio, Fabio
02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)
02286 - Swiss Data Science Center (SDSC) / Swiss Data Science Center (SDSC)
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ETH Bibliography
yes
Altmetrics