Evolvable case-based design: An artificial intelligence system for urban form generation with specific indicators

Abstract

This research proposes a design system that combines a case-based learning algorithm with a rule-based optimization algorithm to automatically generate and revise urban form prototypes based on historical cases and user requirements. The system aims to address the challenges of existing generative methods for urban forms, such as the lack of flexibility and organicity of rule-based methods and the insufficient manipulability and interpretability of the newest GAN-integrated case-based methods. It can help designers generate multiple solutions with specific indicators in the conceptual stage and has the potential to facilitate citizen participation in urban planning and design. This research demonstrates the feasibility and effectiveness of the system through a case study in Shenzhen. The research further extends the discussion about the application of the proposed system and the alternative evolution approach for the next generation of automatic design methods.

Keywords

artificial intelligence case-based design urban form generation generative adversarial networks reinforcement learning

Get full access to this article

View all access options for this article.

References

Berghauser Pont

Haupt

(2007) The Spacemate: Density and the Typomorphology of the Urban Fabric. Urbanism laboratory for cities and regions: progress of research issues in urbanism. 11–26.

Carlhian

(1980) Guides, guideposts and guidelines. In: Old and New Architecture: Design Relationship, 40 ILLUS, 49–68. (General).

Chaillou

(2020) Archigan: artificial intelligence x architecture. Architectural Intelligence. Springer, 117–127.

Dave

Schmitt

Shih

S-G

, et al. (1994) Case-based spatial design reasoning. In: European Workshop on Advances in Case-Based Reasoning. Springer, 198–210.

Dillenburger

(2010) Space Index: A Retrieval-System for Building-Plots.

Domeshek

Kolodner

(1993) Using the points of large cases. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7(2): 87–96.

Goodfellow

Pouget-Abadie

Mirza

, et al. (2014) Generative adversarial nets. Advances in Neural Information Processing Systems 27.

Huang

Belongie

(2017) Arbitrary style transfer in real-time with adaptive instance normalization. Proceedings of the IEEE international conference on computer vision. 1501–1510.

Huang

Zheng

(2018) Architectural Drawings Recognition and Generation through Machine Learning.

10.

Inanc

(2000) Casebook. An Information Retrieval System for Housing Floor Plans.

11.

Isola

Zhu

J-Y

Zhou

, et al. (2017) Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE conference on computer vision and pattern recognition 1125–1134.

12.

Jones

(1977) How my thoughts about design methods have changed during the years. Design methods and Theories 11(1): 48–62.

13.

Jones

Thornley

(1963) Conference on Design Methods: Papers Presented at the Conference on Systematic and Intuitive Methods in Engineering Industrial Design, Architecture and Communications, London, September, 1962.

14.

Karras

Laine

Aila

(2019) A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4401–4410.

15.

Kolodner

(1983) Reconstructive memory: a computer model. Cognitive science 7(4): 281–328.

16.

Langrish

(2016) The Design Methods Movement from Optimism to Darwinism.

17.

Liu

Deng

(2023) Evolvable-Case-based-Design. GitHub. Available at: https://github.com/kekehurry/Evolvable-Case-based-Design.

18.

Makki

Showkatbakhsh

Tabony

, et al. (2019) Evolutionary algorithms for generating urban morphology: variations and multiple objectives. International Journal of Architectural Computing 17(1): 5–35.

19.

Mitchell

(1975) The theoretical foundation of computer-aided architectural design. Environment and Planning B: Planning and Design 2(2): 127–150.

20.

Mitchell

(1977) Computer-aided Architectural Design.

21.

Mitchell

(1990) The Logic of Architecture: Design, Computation, and Cognition. JSTOR.

22.

Mitchell

Steadman

Liggett

(1976) Synthesis and optimization of small rectangular floor plans. Environment and Planning B: Planning and Design 3(1): 37–70.

23.

Narahara

(2017) Collective Construction Modeling and Machine Learning: Potential for Architectural Design.

24.

Nikitko

(2018) StyleGAN — encoder for official TensorFlow implementation. Available at: https://github.com/Puzer/stylegan-encoder.

25.

Pan

Qian

(2020) A preliminary study on the formation of the general layouts on the northern neighborhood community based on GauGAN diversity output generator. In: The International Conference on Computational Design and Robotic Fabrication. Springer, 179–188.

26.

Papanek

(1988) The future isn't what it used to be. Design Issues 5(1): 4–17.

27.

Parish

Müller

(2001) Procedural modeling of cities. Proceedings of the 28th annual conference on Computer graphics and interactive techniques 301–308.

28.

Park

Liu

M-Y

Wang

T-C

, et al. (2019) Semantic image synthesis with spatially-adaptive normalization. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2337–2346.

29.

Quan

(2022) Urban-GAN: an artificial intelligence-aided computation system for plural urban design. Environment and Planning B: Urban Analytics and City Science 23998083221100550.

30.

Ratti

Baker

Steemers

(2005) Energy consumption and urban texture. Energy and Buildings 37(7): 762–776.

31.

Rhee

(2020) Context-Rich Urban Analysis Using Machine Learning: A Case Study in Pittsburgh, PA. Carnegie Mellon University.

32.

Rittel

(1972) On the planning crisis: systems analysis of the 'first and second generations'. Bedriftskonomen 8: 390–396.

33.

Schank

(1983) Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press.

34.

Steadman

(1998) Sketch for an archetypal building. Environment and Planning B: Planning and Design 25(7): 92–105.

35.

Steadman

Mitchell

(2010) Architectural morphospace: mapping worlds of built forms. Environment and Planning B: Planning and Design 37(2): 197–220.

36.

Trancik

(1991) Finding Lost Space: Theories of Urban Design. John Wiley & Sons.

37.

Vanegas

Aliaga

Wonka

, et al. (2010) Modelling the appearance and behaviour of urban spaces. Computer Graphics Forum. Wiley Online Library, 25–42.

38.

Vanegas

Kelly

Weber

, et al. (2012) Procedural generation of parcels in urban modeling. Computer Graphics Forum. Wiley Online Library, 681–690.

39.

Veloso

Krishnamurti

(2020) An Academy of Spatial Agents-Generating Spatial Configurations with Deep Reinforcement Learning.

40.

Wang

T-C

Liu

M-Y

Zhu

J-Y

, et al. (2018) High-resolution image synthesis and semantic manipulation with conditional gans. Proceedings of the IEEE conference on computer vision and pattern recognition. 8798–8807.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.65 MB