DOI: 10.1177/1478077118799491. Design variable analysis and generation for performance-based parametric modeling in architecture

Design variable analysis and generation for performance-based parametric modeling in architecture

10.1177/1478077118799491

Crossref journal-article

SAGE Publications

International Journal of Architectural Computing (179)

Abstract

Many architectural designers recognize the potential of parametric models as a worthwhile approach to performance-driven design. A variety of performance simulations are now possible within computational design environments, and the framework of design space exploration allows users to generate and navigate various possibilities while considering both qualitative and quantitative feedback. At the same time, it can be difficult to formulate a parametric design space in a way that leads to compelling solutions and does not limit flexibility. This article proposes and tests the extension of machine learning and data analysis techniques to early problem setup in order to interrogate, modify, relate, transform, and automatically generate design variables for architectural investigations. Through analysis of two case studies involving structure and daylight, this article demonstrates initial workflows for determining variable importance, finding overall control sliders that relate directly to performance and automatically generating meaningful variables for specific typologies.

Bibliography

Brown, N. C., & Mueller, C. T. (2018). Design variable analysis and generation for performance-based parametric modeling in architecture. International Journal of Architectural Computing, 17(1), 36â52.

Authors 2

Nathan C Brown (first)
Caitlin T Mueller (additional)

References 57 Referenced 30

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{'first-page': '1', 'volume-title': 'Proceedings of the third ACM symposium on solid modeling and applications', 'author': 'Anderl R', 'key': 'bibr8-1478077118799491'} / Proceedings of the third ACM symposium on solid modeling and applications by Anderl R
10.1007/PL00007198
10.1007/s00158-009-0420-2
10.1016/j.cam.2008.12.019
{'key': 'bibr12-1478077118799491', 'volume-title': 'Statistics for experimenters: design, innovation, and discovery', 'author': 'Box GEP', 'year': '2005', 'edition': '2'} / Statistics for experimenters: design, innovation, and discovery by Box GEP (2005)
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{'volume-title': 'Proceedings of the IASS annual symposium 2017', 'author': 'Brown NC', 'key': 'bibr15-1478077118799491'} / Proceedings of the IASS annual symposium 2017 by Brown NC
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{'key': 'bibr17-1478077118799491', 'volume-title': 'Response surface methodology: process and product optimization using designed experiments', 'author': 'Myers RH', 'year': '2009', 'edition': '3'} / Response surface methodology: process and product optimization using designed experiments by Myers RH (2009)
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Dates

Type	When
Created	6 years, 10 months ago (Sept. 28, 2018, 7:53 a.m.)
Deposited	5 months, 3 weeks ago (Feb. 28, 2025, 10:45 a.m.)
Indexed	1 month, 3 weeks ago (June 26, 2025, 5:49 a.m.)
Issued	6 years, 10 months ago (Sept. 28, 2018)
Published	6 years, 10 months ago (Sept. 28, 2018)
Published Online	6 years, 10 months ago (Sept. 28, 2018)
Published Print	6 years, 5 months ago (March 1, 2019)

Funders 0

None

BibTeX

@article{Brown_2018, title={Design variable analysis and generation for performance-based parametric modeling in architecture}, volume={17}, ISSN={2048-3988}, url={http://dx.doi.org/10.1177/1478077118799491}, DOI={10.1177/1478077118799491}, number={1}, journal={International Journal of Architectural Computing}, publisher={SAGE Publications}, author={Brown, Nathan C and Mueller, Caitlin T}, year={2018}, month=sep, pages={36–52} }

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