Data-based generation of residential floorplans using neural networks
- Author
- Louise Deprez (UGent) , Ruben Verstraeten (UGent) and Pieter Pauwels (UGent)
- Organization
- Abstract
- Most generative design applications used in architectural design are developed with rule-based approaches, based on rules collected from expert knowledge and experience. In other domains, machine learning and, more in particular, neural networks have proven their usefulness and added value in replacing these hard-coded rules or improving applications when combining these two strategies. Since the space allocation problem still remains an open research question and common generative design techniques showed their limitations trying to solve this problem, new techniques need to be explored. In this paper, the application of neural networks to solve the space allocation problem for residential floor plans is tested. This research aims to expose the advantages as well as the difficulties of using neural networks by reviewing existing neural network architectures from different domains and by applying and testing them in this new context using a dataset of residential floor plans.
- Keywords
- DESIGN
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8770178
- MLA
- Deprez, Louise, et al. “Data-Based Generation of Residential Floorplans Using Neural Networks.” Design Computing and Cognition’22 : The Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings, Springer, 2023, pp. 321–39, doi:10.1007/978-3-031-20418-0_20.
- APA
- Deprez, L., Verstraeten, R., & Pauwels, P. (2023). Data-based generation of residential floorplans using neural networks. Design Computing and Cognition’22 : The Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings, 321–339. https://doi.org/10.1007/978-3-031-20418-0_20
- Chicago author-date
- Deprez, Louise, Ruben Verstraeten, and Pieter Pauwels. 2023. “Data-Based Generation of Residential Floorplans Using Neural Networks.” In Design Computing and Cognition’22 : The Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings, 321–39. Springer. https://doi.org/10.1007/978-3-031-20418-0_20.
- Chicago author-date (all authors)
- Deprez, Louise, Ruben Verstraeten, and Pieter Pauwels. 2023. “Data-Based Generation of Residential Floorplans Using Neural Networks.” In Design Computing and Cognition’22 : The Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings, 321–339. Springer. doi:10.1007/978-3-031-20418-0_20.
- Vancouver
- 1.Deprez L, Verstraeten R, Pauwels P. Data-based generation of residential floorplans using neural networks. In: Design Computing and Cognition’22 : the Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings. Springer; 2023. p. 321–39.
- IEEE
- [1]L. Deprez, R. Verstraeten, and P. Pauwels, “Data-based generation of residential floorplans using neural networks,” in Design Computing and Cognition’22 : the Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings, Glasgow, UK, 2023, pp. 321–339.
@inproceedings{8770178,
abstract = {{Most generative design applications used in architectural design are
developed with rule-based approaches, based on rules collected from expert
knowledge and experience. In other domains, machine learning and, more
in particular, neural networks have proven their usefulness and added value
in replacing these hard-coded rules or improving applications when
combining these two strategies. Since the space allocation problem still
remains an open research question and common generative design
techniques showed their limitations trying to solve this problem, new
techniques need to be explored. In this paper, the application of neural
networks to solve the space allocation problem for residential floor plans is
tested. This research aims to expose the advantages as well as the difficulties
of using neural networks by reviewing existing neural network architectures
from different domains and by applying and testing them in this new context
using a dataset of residential floor plans.}},
author = {{Deprez, Louise and Verstraeten, Ruben and Pauwels, Pieter}},
booktitle = {{Design Computing and Cognition'22 : the Tenth International Conference on Design Computing and Cognition (DCC’22), Proceedings}},
isbn = {{9783031204173}},
keywords = {{DESIGN}},
language = {{eng}},
location = {{Glasgow, UK}},
pages = {{321--339}},
publisher = {{Springer}},
title = {{Data-based generation of residential floorplans using neural networks}},
url = {{http://doi.org/10.1007/978-3-031-20418-0_20}},
year = {{2023}},
}
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