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Empirical support for problem–solution coevolution in a parametric design environment

Published online by Cambridge University Press:  14 July 2014

Rongrong Yu ,
Ning Gu ,
Michael Ostwald  and
John S. Gero
Rongrong Yu*
Affiliation:
School of Architecture and Built Environment, University of Newcastle, Callaghan, New South Wales, Australia
Ning Gu
Affiliation:
School of Architecture and Built Environment, University of Newcastle, Callaghan, New South Wales, Australia
Michael Ostwald
Affiliation:
School of Architecture and Built Environment, University of Newcastle, Callaghan, New South Wales, Australia
John S. Gero
Affiliation:
School of Architecture and Department of Computer Science, University of North Carolina at Charlotte, Charlotte, North Carolina, USA
*
Reprint requests to: Rongrong Yu, School of Architecture and Built Environment, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia. E-mail: rongrong.yu@uon.edu.au
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Abstract

This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include two potentially significant observations. First, the coevolution process occurs frequently within the design knowledge level (i.e., when using Rhino) and within the rule algorithm level (i.e., when using Grasshopper) of the parametric design process. Second, the designers’ coevolution process was focused on the design knowledge level at the beginning of the design session, while they focused more on the rule algorithm level toward the end of the design session. These results support an improved understanding of the design process that occurs in PDEs.

Keywords

Designers’ Cognitive BehaviorParametric Design EnvironmentsProblem–Solution CoevolutionProtocol Analysis
Type
Regular Articles
Information
AI EDAM , Volume 29 , Issue 1 , February 2015 , pp. 33 - 44
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Abdelmohsen, S., & Do, E.Y.-L. (2009). Analyzing the significance of problem solving expertise and computational tool proficiency in design ideation. Proc. Int. Conf. Computer Aided Architectural Design Futures, CAADFutures 2009, pp. 273–287. Montréal: Presses de l'Université de Montréal.Google Scholar
Abdelsalam, M. (2009). The use of the smart geometry through various design processes: using the programming platform (parametric features) and generative components. Proc. Int. Conf. Arab Society for Computer Aided Architectural Design, ASCAAD 2009, pp. 297–304. Manama, Bahrain: Arab Society for Computer Aided Architectural Design.Google Scholar
Aranda, B., & Lasch, C. (2008). What is parametric to us. In From Control to Design: Parametric/Algorithmic Architecture (Sakamoto, T., & Ferré, A., Eds.), pp. 194–206. Barcelona, Spain: Actar-D.Google Scholar
Asimov, W. (1962). Introduction to Design. Upper Saddle River, NJ: Prentice–Hall.Google Scholar
Atman, C.J., Chimka, J.R., Bursic, K.M., & Nachtmann, H.L. (1999). A comparison of freshman and senior engineering design processes. Design Studies 20(2), 131–152.Google Scholar
Boland, R.J., Collopy, F., Kalle, L., & Youngjin, Y. (2008). Managing as designing: lessons for organization leaders from the design practice of Frank O. Gehry. Design Issues 24(1), 10–25.Google Scholar
Chen, S.-C. (2001). The role of design creativity in computer media. Proc. Int. Conf. Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2001, pp. 226–231. Helsinki: eCAADe in cooperation with Mediatecture.Google Scholar
Chi, M.T.H. (1997). Quantifying qualitative analyses of verbal data: a practical guide. Learning Science 6(3), 271–315.Google Scholar
Chien, S.-F., & Yeh, Y.-T. (2012). On creativity and parametric design—a preliminary study of designer's behaviour when employing parametric design tools. Proc. Int. Conf. Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2012, pp. 245–253. Prague: eCAADe in cooperation with Mediatecture.Google Scholar
Corne, D., Smithers, T., & Ross, P. (1994). Solving design problems by computational exploration. In Formal Design Methods for Computer-Aided Design (Gero, J., & Tyugy, N., Eds.), pp. 249–270. Amsterdam: Elsevier.Google Scholar
Cross, N. (2011). Design Thinking: Understanding How Designers Think and Work. New York: Berg.CrossRefGoogle Scholar
Cross, N., & Cross, C. (1998). Expertise in engineering design. Research in Engineering Design 10(3), 141–149.Google Scholar
Dorst, K., & Cross, N. (2001). Creativity in the design process: coevolution of problem–solution. Design Studies 22(5), 425–437.Google Scholar
Ericsson, K.A., & Simon, H.A. (1993). Protocol Analysis: Verbal Reports as Data. Cambridge, MA: MIT Press.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gero, J.S., & Kannengiesser, U. (2004). The situated function–behaviour–structure framework. Design Studies 25(4), 373–391.Google Scholar
Gero, J.S., Kannengiesser, U., & Pourmohamadi, M. (2014). Commonalities across designing: empirical results. Proc. Design Computing and Cognition ‘12 (Gero, J.S., Ed.), pp. 285–302. Berlin: Springer.Google Scholar
Gero, J.S., & McNeill, T. (1998). An approach to the analysis of design protocols. Design Studies 19(1), 21–61.Google Scholar
Hernandez, C.R.B. (2006). Design procedure: a computational framework for parametric design and complex shapes in Architecture. PhD Thesis. Cambridge, MA: MIT.Google Scholar
Iordanova, I., Tidafi, T., Guité, M., De Paoli, G., & Lachapelle, J. (2009). Parametric methods of exploration and creativity during architectural design: a case study in the design studio. Proc. Int. Conf. Computer Aided Architectural Design Futures, CaadFutures 2009, pp. 423–439. Montréal: Presses de l'Université de Montréal.Google Scholar
Jiang, H. (2012). Understanding senior design students’ product conceptual design activities —a comparison between industrial and engineering design students. PhD Thesis. Singapore: National University of Singapore.Google Scholar
Jiang, H., Gero, J.S., & Yen, C.C. (2014). Exploring designing styles using a problem–solution index. Proc. Design Computing and Cognition ‘12 (Gero, J.S., Ed.), pp. 85–101. Berlin: Springer.Google Scholar
Kan, J.W.T., & Gero, J.S. (2008). Acquiring information from linkography in protocol studies of designing. Design Studies 29(4), 315–337.Google Scholar
Kan, J.W.T., & Gero, J.S. (2009). Using the FBS ontology to capture semantic design information in design protocol studies. In About Designing: Analysing Design Meetings (McDonnell, J., & Lloyd, P., Eds.), pp. 213–229. New York: Taylor & Francis.Google Scholar
Kan, J.W.T., & Gero, J.S. (2012). Studying software design cognition. In Software Designers in Action: A Human-Centric Look at Design Work (Petre, M., & van der Hoek, A., Eds.), p. 61–77. Abingdon: Chapman & Hall/CRC.Google Scholar
Karle, D., & Kelly, B. (2011). Parametric thinking. Proc. Int. Conf. Parametricism (SPC) ACADIA Regional 2011, Paper No. 109, Lincoln, NE, March 11–12.Google Scholar
Kolarevic, B. (2003). Architecture in the Digital Age: Design and Manufacturing. New York: Spon Press.Google Scholar
Lammi, M. (2011). Characterizing high school students’ systems thinking in engineering design through the function–behavior–structure (FBS) framework. PhD Thesis. Logan, UT: Utah State University.Google Scholar
Lawson, B. (1997). How Designers Think: The Design Process Demystified. Oxford: Architectural Press.Google Scholar
Lee, J.H., Gu, N., Jupp, J., & Sherratt, S. (2012). Evaluating creativity in parametric design processes and products: a pilot study. Proc. Int. Conf. Design Computing and Cognition, DCC'12. College Station, TX: Springer .Google Scholar
Maher, M.L., & Kundu, S. (1993). Adaptive design using a genetic algorithm. In Formal Design Methods for Computer-Aided Design (Gero, J.S., & Sudweeks, F., Eds.), pp. 240–248. Sydney: University of Sydney, Key Centre of Design Computing.Google Scholar
Maher, M.L., & Poon, J. (1996). Modelling design exploration as coevolution. Microcomputers in Civil Engineering 11(3), 195–210.Google Scholar
Maher, M.L., Poon, J., & Boulanger, S. (1996). Formalising design exploration as coevolution: a combined gene approach. In Advances in Formal Design Methods for CAD (Gero, J.S., & Sudweeks, F., Eds.), pp. 3–30. London: Chapman & Hall.Google Scholar
Maher, M.L., & Tang, H.H. (2003). Coevolution as a computational and cognitive model of design. Research in Engineering Design 14(1), 47–63.Google Scholar
Mitchell, W.J., Inouye, A.S., & Blumenthal, M.S. (2003). Beyond Productivity: Information Technology, Innovation, and Creativity. Washington, DC: National Academies Press.Google Scholar
Ottchen, C. (2009). The future of information modelling and the end of theory: less is limited, more is different. Architectural Design 79, 22–27.Google Scholar
Qian, C.Z., Chen, V.Y., & Woodbury, R.F. (2007). Participant observation can discover design patterns in parametric modeling. Proc. Int. Conf. Association for Computer Aided Design in Architecture, ACADIA2007, pp. 230–241. Halifax, NS: Riverside Architectural Press and Tuns Press.Google Scholar
Sanguinetti, P., & Kraus, C. (2011). Thinking in parametric phenomenology. Proc. Int. Conf. Parametricism (SPC) ACADIA Regional 2011, Paper No. 39, Lincoln, NE, March 11–12.Google Scholar
Schnabel, M.A. (2007). Parametric designing in architecture. Proc. Int. Conf. Computer Aided Architectural Design Futures, CAADFutures 2007, pp. 237–250. Sydney: Springer.Google Scholar
Schön, D.A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies 13(2), 135–156.Google Scholar
Schön, D.A. (1983). The Reflective Practitioner: How Professionals Think in Action. New York: Basic Books.Google Scholar
Simon, H.A. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Simon, H.A. (1973). The structure of ill-structured problems. Artificial Intelligence 4(3–4), 181–204.Google Scholar
Suwa, M., Gero, J.S., & Purcell, T. (2000). Unexpected discoveries and S-invention of design requirements: important vehicles for a design process. Design Studies 21(6), 539–567.Google Scholar
Suwa, M., & Tversky, B. (1997). What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18(4), 385–403.Google Scholar
Tang, H.H., Lee, Y.Y., & Gero, J.S. (2011). Comparing collaborative co-located and distributed design processes in digital and traditional sketching environments: a protocol study using the function–behaviour–structure coding scheme. Design Studies 32(1), 1–29.Google Scholar
Woodbury, R. (2010). Elements of Parametric Design. New York: Routledge.Google Scholar
Yu, R., Gu, N., & Ostwald, M.J. (2012). Using situated FBS ontology to explore designers’ patterns of behavior in parametric envrionments. Journal of Information Technology in Construction 17, 271–282.Google Scholar