Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T09:08:58.326Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alfaris, A., & Merello, R. (2008). The generative multi-performance design system. Proc. Silicon+ Skin: Biological Processes and Computation (28th ACADIA), Minneapolis.Google Scholar
Almusharaf, M., & Mahjoub, E. (2010). A performance-based design approach for early tall building form development. In CAAD|Cities|Sustainability: 5th Int. Conf. Proc. Arab Society for Computer Aided Architectural Design, pp. 39–50. Fez, Morocco: Arab Society for Computer Aided Architectural Design.Google Scholar
Arvin, S.A., & House, D.H. (2002). Modeling architectural design objectives in physically based space planning. Automation in Construction 11(2), 213–225.Google Scholar
Bafna, S. (1999). The morphology of early modernist residential plans: geometry and genotypical trends in Mies van der Rohe's designs. Proc. Space Syntax-II Int. Symposium, Brasilia.Google Scholar
Coorey, B. (2010). Scalability: parametric strategies from exoskeletons to the city. In New Frontiers: Proc. 15th Conf. Computer Aided Architectural Design Research in Asia (Dave, B., Li, A.I., Gu, N., & Park, H.-J., Eds.), Hong Kong, April 7–10.Google Scholar
Coorey, B., & Jupp, J. (2013). A schema for capturing and comparing parametric spatial data. Open Systems: Proc. 18th Conf. Computer Aided Architectural Design Research in Asia (CAADRIA) (Stouffs, R., Janssen, P.H.T., Roudavski, S., & Tunçer, B., Eds.), Singapore, May 15–17.Google Scholar
Eloy, S., & Duarte, J.P. (2011). Transformation grammar for housing rehabilitation. Nexus Network Journal 13(1), 49–71.CrossRefGoogle Scholar
Gero, J.S. (1994). Towards a model of exploration in computer-aided design. In Formal Design Methods for CAD (Gero, J.S., & Tyugu, E., Eds.), pp. 315–336. Amsterdam: North-Holland.Google Scholar
Gun, O. (2010). Geometric gestures. In Elements of Parametric Design (Woodbury, R., Ed.). London: Routledge.Google Scholar
Heitor, T.V., Duarte, J.P., & Rafaela, M. (2004). Combining grammars and space syntax: formulating, generating and evaluating designs. International Journal of Architectural Computing 2(4), 492–515.Google Scholar
Hensel, M., Menges, A., & Weinstock, M. (2010). Emergent Technologies and Design. New York: Routledge.Google Scholar
Hernandez, C. (2006). Thinking parametric design: introducing parametric Gaudi. Design Studies 27(3), 309–324.Google Scholar
Hillier, B., & Hanson, J. (1984). The Social Logic of Space. Cambridge: Cambridge University Press.Google Scholar
Janssen, P.H.T. (2005). A design method and computational architecture for generating and evolving building designs. PhD Thesis, Hong Kong Polytechnic University.Google Scholar
Kruger, C., & Cross, N. (2006). Solution driven versus problem driven design: strategies and outcomes, Design Studies 27(5), 527–548.Google Scholar
Littlefield, D. (2008). Space Craft: Developments in Architectural Computing. Marylebone: Royal Institute of British Architects.Google Scholar
Ostwald, M.J. (2011). A justified plan graph analysis of the early houses (1975–1982) of Glenn Murcutt. Nexus Network Journal 13(3), 737–762.Google Scholar
Park, S.M., Elnimeiri, M., Sharpe, D.C., & Krawczyk, R.J. (2005). Tall Building Form Generation by Parametric Design Process. Chicago: Illinois Institute of Technology.Google Scholar
Peters, B. (2010). The new Elephant House. In Elements of Parametric Design (Woodbury, R., Ed.). London: Routledge.Google Scholar
Piker, Daniel. (2013). Kangaroo Plugin for Grasshopper [Computer Software]. Accessed at http://www.grasshopper3d.com/group/kangaroo October 1, 2013.Google Scholar
Sakamoto, T., & Ferré, A. (2008). From Control to Design: Parametric/Algorithmic Architecture. Barcelona: Actar-D.Google Scholar
Schumacher, P. (Ed.). (2012). The Autopoiesis of Architecture: A New Framework for Architecture, Vol. 2. Chichester: Wiley.Google Scholar