| id |
ijac202220203 |
| authors |
Dzieduszyński, Tomasz |
| year |
2022 |
| title |
Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions |
| source |
International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 196–215 |
| summary |
A substantial part of architectural and urban design involves processing of compositional interdependenciesand contexts. This article attempts to isolate the problem of spatial composition from the broader category ofsynthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenariosvarying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation ofcontext-fitting spatial composition. The technique can be applied for the extraction of compositionalprinciples from the architectural, urban, or artistic contexts and may facilitate the design-related decisionmaking by complementing the required expert analysis |
| keywords |
Spatial composition, architecture, convolutional neural network, ordering principles, machine learning, image generation, design, CAAD |
| series |
journal |
| references |
Content-type: text/plain
|
Benedikt M (1979)
 To take hold of space: Isovists and isovist fields
, Environ Plann B Plann Des 1979; 6(1): 47–65.
|
|
|
|
|
Caron M, Touvron H, Misra I, et al (2021)
Emerging properties in self-supervised vision transformers
, arXiv Preprint. arXiv 2021.
|
|
|
|
|
Chen M, Radford A, Child R, et al (2020)
Generative pretraining from pixels
, Proc 37th Int Conf Machine Learn 2020; 119: 1691–1703.
|
|
|
|
|
Ching FDK (2007)
Principles: Architecture–Form, Space and Order
, 3rd ed New Jersey, NY: John Willey & Sons, Inc.; 2007, pp 338–402.
|
|
|
|
|
Deng J, Dong W, Socher R, et al (2009)
Image net: A large-scale hierarchical image database
, IEEE Conference Computer Vision and Pattern Recognition June, 2009, Miami, Florida: 248–255.
|
|
|
|
|
Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020)
An image is worth 16x16 Words: Transformers for image recognition at scale
, arXiv Preprint. arXiv 2020.
|
|
|
|
|
Eisenstadt V, Langenhan C and Althoff KD (2019)
Generation of floor plan variations with convolutional neural networks and case-based reasoning-An approach for transformative adaptation of room configurations within a framework for support of early conceptual design phases
, Proc 37th eCAADe 23rd SIGraDi Conference December 1, 2019; 2: 79–84.
|
|
|
|
|
Fukushima K (1980)
Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
, Biol Cybernetics 1980; 36: 193–202.
|
|
|
|
|
Gatys AL, Ecker SA and Bethge M (2016)
A neural algorithm of artistic style
, J Vis 2016; 16(326).
|
|
|
|
|
Gero JS (1996)
Artificial intelligence in computer-aided design: Progress and prognosis
, Computer-Aided Des 1996; 28(3): 153–154.
|
|
|
|
|
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al (2014)
Generative adversarial networks
, arXiv Preprint. arXiv 2014.
|
|
|
|
|
Hinton GE, Rumelhart DE and Williams RJ (1986)
Learning representations by back-propagating errors
, Nature 1986; 323(9): 533–536.
|
|
|
|
|
Huber PJ (1964)
Robust estimation of location parameter
, Ann Stat 1964; 53(1): 73–101.
|
|
|
|
|
Krizhevsky A, Sutskever I and Hinton GE (2012)
ImageNet Classification with Deep Convolutional Neural Networks
, Adv Neural Inf Process Syst 2012; 25(2): 84–90.
|
|
|
|
|
Long J, Shelhamer E and Darrell T (2015)
Fully convolutional networks for semantic segmentation
, IEEE CVPR 2015: 3431–3440.
|
|
|
|
|
Mordvintsev A, Olah C and Tyka M (2015)
Inceptionism: Going deeper into neural networks
, Google Research Blog, 2015.
|
|
|
|
|
Mostafa A and Al-Kaseem B (2021)
Fill in the blanks: Deep convolutional generative adversarial networks to investigate the virtual design space of historical islamic patterns
, 9th ASCAAD Conf Proc 2021: 614–621.
|
|
|
|
|
Ng JMY, Khean N and Madden D (2019)
Optimising image classification: Implementation of convolutional neural network algorithms to distinguish between plans and sections within the Architectural, Engineering and Construction (AEC) industry
, Intelligent & Informed - 24th CAADRIA Conference, April, 2019, Victoria University of Wellington, pp. 795–804.
|
|
|
|
|
Peng W, Zhang F and Nagakura T (2017)
Machines’ perception of space: Employing 3D isovist methods and a convolutional neural network in architectural space classification
, ACADIA 2017: 474–481.
|
|
|
|
|
Ramesh A, Pavlov M, Goh G, et al (2021)
Zero-shot text-to-image generation
, arXiv Preprint. arXiv:2102.12092 2021
|
|
|
|
| last changed |
2024/04/17 14:29 |
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