Thinking Topologically at Early Stage Parametric Design
Parametric modelling tools have allowed architects and engineers to explore complex geometries with relative ease at the early stage of the design process. Building designs are commonly created by authoring a visual graph representation that generates building geometry in model space. Once a graph is constructed, design exploration can occur by adjusting metric sliders either manually or automatically using optimization algorithms in combination with multi-objective performance criteria. In addition, qualitative aspects such as visual and social concerns may be included in the search process. The authors propose that whilst this way of working has many benefits if the building type is already known, the inflexibility of the graph representation and its top-down method of generation are not well suited to the conceptual design stage where the search space is large and constraints and objectives are often poorly defined. In response, this paper suggests possible ways of liberating parametric modelling tools by allowing changes in the graph topology to occur as well as the metric parameters during building design and optimisation.
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- AISH, R. 2000. Migration from an individual to an enterprise computing model and its implications for AEC Research. Berkeley-Stanford CE&M Workshop.Google Scholar
- COENDERS, J.L. 2011. NetworkedDesign, Next Generation Infrastructure for Design Modelling. In Proceedings of the Design Modelling Symposium 2011, Springer-Verlag Berlin Heidelberg, Berlin, Germany, 39–46.Google Scholar
- DAVIS, D., BURRY, M., AND BURRY, J. 2011a. Untangling Parametric Schemata: Enhancing Collaboration through Modular Programming. In CAAD Futures 2011, Designing Together, 55–68.Google Scholar
- DAVIS, D., BURRY, M., AND BURRY, J. 2011b. The flexibility of logic programming: Parametrically regenerating the Sagrada Família. In Proceedings of the 16th International Conference on Computer Aided Architectural Design Research in Asia, 29–38.Google Scholar
- DEB, K. 2001. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons.Google Scholar
- DELANDA, M. 2002. Deleuze and the use of the genetic algorithm in architecture. In Designing for a Digital World, 117–120.Google Scholar
- EVINS, R., JOYCE, S., POINTER, P., SHARMA, S., VAIDYANATHAN, R., WILLIAMS, C. 2012. Multi-objective design optimisation: getting more for less. Proceedings of the Institution of Civil Engineers Civil engineering Special issue 165(5), 5–10.Google Scholar
- HOLZER, D., HOUGH, R. AND BURRY, M. 2008. Parametric Design & Optimisation for Early Design Exploration. In International Journal of Architectural Computing, 04(05), 638.Google Scholar
- KOZA, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.Google Scholar
- MUELLER, V. 2011. Distributed Perspectives for Intelligent Conceptual Design. In Distributed Intelligence in Design, Wiley-Blackwell, Oxford, UK.Google Scholar
- ROWE, P. 1987. Design Thinking. MIT Press.Google Scholar
- RUTTEN, D. 2010. Evolutionary Principles applied to Problem Solving http://www.grasshopper3d.com/profiles/blogs/evolutionary-principles accessed 14th May 2012.
- SHEA, K., AISH, R. AND GOURTOVAIAL, M. 2003. Towards performance based generative design tools. In Digital Design, 21st eCAADe Conference proceedings, 103–110.Google Scholar
- VAN LEEUWEN, J. 1990. Graph algorithms. In Handbook of Theoretical Computer Science Volume A: Algorithms & Complexity, Chapter 10, Elsevier, Amsterdam.Google Scholar
- WOODBURY, R., AISH, R. AND KILIAN, 2007. Some Patterns for Parametric Modeling. In Proceedings of 27th ACADIA Conference, Association for Computer Aided Design in Architecture, Halifax.Google Scholar