Generative spatial performance design system

Coorey, BP; Jupp, JR

Generative spatial performance design system

Coorey, BP Jupp, JR

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Publication Type:: Journal Article
Citation:: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 2014, 28 (3), pp. 277 - 283
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Coorey, BP	en_US
dc.contributor.author	Jupp, JR https://orcid.org/0000-0002-3774-261X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 2014, 28 (3), pp. 277 - 283	en_US
dc.identifier.issn	0890-0604	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34102
dc.description.abstract	Architectural spatial design is a wicked problem that can have a multitude of solutions for any given brief. The information needed to resolve architectural design problems is often not readily available during the early conceptual stages, requiring proposals to be evaluated only after an initial solution is reached. This solution-driven design approach focuses on the generation of designs as a means to explore the solution space. Generative design can be achieved computationally through parametric and algorithmic processes. However, utilizing a large repertoire of organiational patterns and design precedent knowledge together with the precise criteria of spatial evaluation can present design challenges even to an experienced architect. In the implementation of a parametric design process lies an opportunity to supplement the designer's knowledge with computational decision support that provides real-time spatial feedback during conceptual design. This paper presents an approach based on a generative multiperformance framework, configured for generating and optimizing architectural designs based on a precedent design. The system is constructed using a parametric modeling environment enabling the capture of precedent designs, extraction of spatial analytics, and demonstration of how populations can be used to drive the generation and optimization of alternate spatial solutions. A pilot study implementing the complete workflow of the system is used to illustrate the benefits of coupling parametric modeling with structured precedent analysis and design generation. Copyright © Cambridge University Press 2014.	en_US
dc.relation.ispartof	Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM	en_US
dc.relation.isbasedon	10.1017/S0890060414000225	en_US
dc.subject.classification	Design Practice & Management	en_US
dc.title	Generative spatial performance design system	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	28	en_US
utslib.for	1201 Architecture	en_US
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	1203 Design Practice and Management	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Professional Practice and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CCDP - Contemporary Design Practice
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

Architectural spatial design is a wicked problem that can have a multitude of solutions for any given brief. The information needed to resolve architectural design problems is often not readily available during the early conceptual stages, requiring proposals to be evaluated only after an initial solution is reached. This solution-driven design approach focuses on the generation of designs as a means to explore the solution space. Generative design can be achieved computationally through parametric and algorithmic processes. However, utilizing a large repertoire of organiational patterns and design precedent knowledge together with the precise criteria of spatial evaluation can present design challenges even to an experienced architect. In the implementation of a parametric design process lies an opportunity to supplement the designer's knowledge with computational decision support that provides real-time spatial feedback during conceptual design. This paper presents an approach based on a generative multiperformance framework, configured for generating and optimizing architectural designs based on a precedent design. The system is constructed using a parametric modeling environment enabling the capture of precedent designs, extraction of spatial analytics, and demonstration of how populations can be used to drive the generation and optimization of alternate spatial solutions. A pilot study implementing the complete workflow of the system is used to illustrate the benefits of coupling parametric modeling with structured precedent analysis and design generation. Copyright © Cambridge University Press 2014.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/34102