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Generative spatial performance design system

Published online by Cambridge University Press:  22 July 2014

Benjamin P. Coorey  and
Julie R. Jupp
Benjamin P. Coorey*
Affiliation:
School of Architecture, University of Technology, Sydney, Sydney, Australia
Julie R. Jupp
Affiliation:
School of the Built Environment, University of Technology, Sydney, Sydney, Australia
*
Reprint requests to: Benjamin P. Coorey, School of Architecture, University of Technology Sydney, Sydney, Australia. E-mail: Benjamin.Coorey@uts.edu.au
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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.

Keywords

Generative DesignParametric DesignPerformance Driven DesignSpace SyntaxSpatial Analysis
Type
Research Article
Information
AI EDAM , Volume 28 , Issue 3: Computational Design Synthesis , August 2014 , pp. 277 - 283
Copyright
Copyright © Cambridge University Press 2014 

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