Tarabishy, S;
Psarras, S;
Kosicki, M;
Tsigkari, M;
(2020)
Deep learning surrogate models for spatial and visual connectivity.
International Journal of Architectural Computing
, 18
(1)
pp. 53-66.
10.1177/1478077119894483.
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Abstract
Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This article investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end, we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks.
Type: | Article |
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Title: | Deep learning surrogate models for spatial and visual connectivity |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1177/1478077119894483 |
Publisher version: | http://dx.doi.org/10.1177/1478077119894483 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Algorithmic and evolutionary techniques, performance and simulation, machine learning |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture |
URI: | https://discovery.ucl.ac.uk/id/eprint/10114521 |
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