| id |
ijac202321206 |
| authors |
Pouliou, Panagiota; Horvath, Anca-Simona; Palamas, George |
| year |
2023 |
| title |
Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks |
| source |
International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 315–336 |
| summary |
Abstract The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design |
| keywords |
computational design, architecture, machine learning, design process, GNN, point cloud, generative design, artificial intelligence |
| series |
journal |
| references |
Content-type: text/plain
|
Achlioptas P, Diamanti O, Mitliagkas Iet al (2018)
 Learning representations and generative models for 3D point clouds
, International conference on machine learning. https://proceedings.mlr.press/: PMLR, 2018, pp. 40–49.
|
|
|
|
|
Al-Qawasmi J and Hadjri K (2007)
Architecture in the digital age: The effect of digital media on the design, production and evaluation of the built environment
, Open House International 2007; 32(2): 4–6.
|
|
|
|
|
Algeciras-Rodriguez J (2018)
Stochastic Hybrids-From references to design options through Self-Organizing Maps methodology
, Lodz, Poland: Proceedings of the 36th eCAADe Conference, 2018, 1.
|
|
|
|
|
As I, Pal S and Basu P (2018)
Artificial intelligence in architecture: Generating conceptual design via deep learning
, International Journal of Architectural Computing 2018; 16(4): 306–327.
|
|
|
|
|
Belem C, Santos L and Leitao A (2019)
On the impact of machine learning: architecture without architects?
, Daejeon, Korea: 18th International Conference, CAAD Futures 2019, Proceedings, 2019.
|
|
|
|
|
Bernstein P (2022)
Machine learning: architecture in the age of artificial intelligence
, Abingdon, Oxfordshire: Routledge, 2022.
|
|
|
|
|
Boden MA (2005)
What is creativity? Creativity in human evolution and prehistory
, Abingdon, Oxfordshire: Routledge, 2005, pp. 27–55.
|
|
|
|
|
Boden MA (2009)
Computer Models of Creativity
, AI Mag 2009; 30(3): 23–34.
|
|
|
|
|
Bridson R (2007)
Fast Poisson disk sampling in arbitrary dimensions
, SIGGRAPH Sketches 2007; 10(1): 1.
|
|
|
|
|
Chaillou S (2022)
Artificial intelligence and architecture: from research to practice
, Basel: De Gruyter: Birkhaaser, 2022
|
|
|
|
|
Creswell A, White T, Dumoulin V, et al (2018)
Generative adversarial networks: An overview
, IEEE Signal Process Mag 2018; 35(1): 53–65.
|
|
|
|
|
Croce V, Caroti G and De Luca L, et al (2021)
From the semantic point cloud to heritage-building information modeling: A semiautomatic approach exploiting machine learning
, Remote Sensing 2021; 13(3): 461.
|
|
|
|
|
Daszykowski M, Walczak B and Massart DL (2002)
On the optimal partitioning of data with K-means, growing K-means, neural gas, and growing neural gas
, J Chem Inf Comput Sci 2002; 42(6): 1378–1389.
|
|
|
|
|
Del Campo M and Leach N (eds) (2022)
Machine Hallucinations: Architecture and Artificial Intelligence
, Hoboken, New Jersey: John Wiley & Sons, 2022.
|
|
|
|
|
Del Campo M, Carlson A and Manninger S (2021)
Towards hallucinating machines-designing with computational vision.
, International Journal of Architectural Computing 2021; 19(1): 88–103
|
|
|
|
|
Deutsch R (2011)
BIM and integrated design: strategies for architectural practice
, Hoboken, New Jersey: John Wiley & Sons, 2011.
|
|
|
|
|
dPrix W, Schmidbaur K and Bolojan D, et al (2022)
The legacy sketch machine: from artificial to architectural intelligence
, Architectural Design 2022; 92: 14–21.
|
|
|
|
|
Dunne A and Raby F (2013)
Speculative everything design, fiction, and social dreaming
, Cambridge, Massachusetts: MIT Press, 2013.
|
|
|
|
|
Geiger A, Lenz P and Urtasun R (2012)
Are we ready for autonomous driving? the kitti vision benchmark suite
, 2012 IEEE conference on computer vision and pattern recognition. New York: IEEE, 2012, pp. 3354–3361.
|
|
|
|
|
Goodfellow I, Pouget-Abadie J, Mirza M, et al (2020)
Generative adversarial networks
, Commun ACM 2020; 63(11): 139–144.
|
|
|
|
| last changed |
2024/04/17 14:30 |
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