Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-16T17:59:55.710Z Has data issue: false hasContentIssue false
  • English
  • Français

Quantifying diversity in parametric design: a comparison of possible metrics

Published online by Cambridge University Press:  30 May 2018

Nathan C. Brown Open the ORCID record for Nathan C. Brown [Opens in a new window]  and
Caitlin T. Mueller
Nathan C. Brown*
Affiliation:
Department of Architecture, Massachusetts Institute of Technology, Building Technology Program, Cambridge, MA 02139, USA
Caitlin T. Mueller
Affiliation:
Department of Architecture, Massachusetts Institute of Technology, Building Technology Program, Cambridge, MA 02139, USA
*
Author for correspondence: Nathan C. Brown, E-mail: ncbrown@mit.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

To be useful for architects and related designers searching for creative, expressive forms, performance-based digital tools must generate a diverse range of design solutions. This gives the designer flexibility to choose from a number of high-performing designs based on aesthetic preferences or other priorities. However, there is no single established method for measuring diversity in the context of computational design, especially in the field of architecture. This paper explores different metrics for quantifying diversity in parametric design, which is an increasingly common digital approach to early-stage exploration, and tests how human users perceive these diversity measurements. It first provides a review of existing methodologies for measuring diversity and describes how they can be adapted for parametrically formulated design spaces. This paper then tests how these different metrics align with human perception of design diversity through an online visual survey. Finally, it offers a quantitative comparison between the different methods and a discussion of their attributes and potential applications. In general, the comparison indicates that at the level of diversity difference that becomes visually meaningful to humans, the measurable difference between metrics is small. This paper informs future researchers, developers, and designers about the measurement of diversity in parametric design, and can stimulate further studies into the perception of diversity within sets of design options, as well as new design methodologies that combine architectural novelty and performance.

Keywords

Computational designdesign diversitydesign space explorationhuman–computer interactionnovelty and dissimilarity
Type
Research Article
Information
AI EDAM , Volume 33 , Issue 1 , February 2019 , pp. 40 - 53
Copyright
Copyright © Cambridge University Press 2018 

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

Aggarwal, CC, Hinneburg, A and Keim, DA (2001) On the surprising behavior of distance metrics in high dimensional space. ICDT 1, 420–434.Google Scholar
Agresti, A and Agresti, BF (1978) Statistical analysis of qualitative variation. Sociological Methodology 9, 204–237.Google Scholar
Amabile, TM (1982) Social psychology of creativity: a consensual assessment technique. Journal of Personality and Social Psychology 43(5), 997–1013.Google Scholar
Autodesk (2013) Dynamo. San Rafael, CA: Autodesk, Inc.Google Scholar
Balling, R (1999) Design by Shopping: A New Paradigm? In Proceedings of the Third World Congress of structural and multidisciplinary optimization (WCSMO-3).Google Scholar
Berkhin, P (2006) Survey of clustering data mining techniques. In Kogan, J, Nicholas, C and Teboulle, M (eds). Grouping Multidimensional Data. Berlin, Heidelberg: Springer, pp. 25–71.Google Scholar
Brown, N and Mueller, C (2016) Design for structural and energy performance of long span buildings using geometric multi-objective optimization. Energy and Buildings 127, 748–761.Google Scholar
Brown, N, Tseranidis, S and Mueller, C (2015) Multi-objective Optimization for Diversity and Performance in Conceptual Structural Design. In Proceedings of the International Association for Shell and Spatial Structures Symposium 2015: Future Visions. Amsterdam: IASS.Google Scholar
Buhrmester, M, Kwang, T and Gosling, SD (2011) Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science 6(1), 3–5.Google Scholar
Burry, M (1996) Parametric design and the Sagrada Familia. Architectural Research Quarterly 1(4), 70–81.Google Scholar
Caldas, LG and Norford, LK (2003) Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems. Journal of Solar Energy Engineering 125(3), 343–351.Google Scholar
Coley, DA and Schukat, S (2002) Low-energy design: combining computer-based optimisation and human judgement. Building and Environment 37(12), 1241–1247.Google Scholar
Colley, WN (2002) Colley's Bias Free College Football Ranking Method: The Colley Matrix Explained. Princeton, NJ: Elsevier.Google Scholar
Cornwell, WK, Schwilk, DW and Ackerly, DD (2006) A trait-based test for habitat filtering: convex hull volume. Ecology 87(6), 1465–1471.Google Scholar
Cvetkovic, D and Parmee, IC (2002) Preferences and their application in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 6(1), 42–57.Google Scholar
Dean, DL, Hender, JM, Rodgers, TL and Santanen, E (2006) Identifying good ideas: constructs and scales for idea evaluation. Journal of Association for Information Systems 7(10), 646–699.Google Scholar
Deb, K, Pratap, A, Agarwal, S and Meyarivan, T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197.Google Scholar
Design (2017) In Cambridge Dictionary of American English. Cambridge, UK: Cambridge University Press.Google Scholar
Erhan, H, Wang, IY and Shireen, N (2015) Harnessing design space: a similarity-based exploration method for generative design. International Journal of Architectural Computing 13(2), 217–236.Google Scholar
Gane, V (2004) Parametric Design – A Paradigm Shift? Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
Goldschmidt, G (1994) On visual design thinking: the vis kids of architecture. Design Studies 15(2), 158–174.Google Scholar
Google Maps (2017) www.google.com/maps.Google Scholar
Häggman, A, Tsai, G, Elsen, C, Honda, T and Yang, M (2015) Connections between the design tool, design attributes, and user preferences in early stage design. Journal of Mechanical Design 137(7), 071408-1–071408-13.Google Scholar
Hill, MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54(2), 427–432.Google Scholar
Holzer, D, Hough, R and Burry, M (2008) Parametric design and structural optimisation for early design exploration. International Journal of Architectural Computing 5(4), 625–644.Google Scholar
Horn, D and Salvendy, G (2009) Measuring consumer perception of product creativity: impact on satisfaction and purchasability. Human Factors and Ergonomics in Manufacturing 19(3), 223–240.Google Scholar
Jain, AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31(8), 651–666.Google Scholar
Jost, L (2006) Entropy and diversity. Oikos 113(2), 363–375.Google Scholar
Kan, JWT and Gero, JS (2018) Characterizing innovative processes in design spaces through measuring the information entropy of empirical data from protocol studies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32(1), 32–43.Google Scholar
Kokare, M, Chatterji, BN and Biswas, PK (2003) Comparison of Similarity Metrics for Texture Image Retrieval. In TENCON 2003: Conference on Convergent Technologies for the Asia-Pacific Region. Bangalore, India.Google Scholar
Kolarevic, B (ed.) (2003) Architecture in the Digital age: Design and Manufacturing. Spon Press, an imprint of the Taylor & Francis Group, New York and London.Google Scholar
Kudrowitz, BM and Wallace, D (2017) Assessing the quality of ideas from prolific, early-stage product ideation. Journal of Engineering Design 24(2), 120–139.Google Scholar
Layman, CA, Arrington, DA, Montana, CG and Post, DM (2007) Can stable isotope ratios provide for community-wide measures of trophic structure. Ecology 88(1), 42–48.Google Scholar
Lehman, J and Stanley, KO (2011) Abandoning objectives: evolution through the search for novelty alone. Evolutionary Computation 19(2), 189–223.Google Scholar
Macomber, B and Yang, M (2011) The Role of Sketch Finish and Style in User Responses to Early Stage Design Concepts. In Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference. Washington, DC.Google Scholar
Marks, W (1997) Multicriteria optimisation energy-saving buildings. Building and Environment 32(4), 331–339.Google Scholar
McCaffrey, T and Spector, L (2018) An approach to human–machine collaboration in innovation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 32(1), 1–15.Google Scholar
McCune, B and Grace, JB (2002) Analysis of Ecological Communities. Oregon, USA, Gleneden Beach: MjM Software.Google Scholar
Monedero, J (2000) Parametric design: a review and some experiences. Automation in Construction 9, 369–377.Google Scholar
Mueller, CT and Ochsendorf, JA (2015) Combining structural performance and designer preferences in evolutionary design space exploration. Automation in Construction 52, 70–82.Google Scholar
Oxman, R (2008) Design: current practices and research issues. International Journal of Architectural Computing 6(1), 1–17.Google Scholar
Paolacci, G, Chandler, J and Ipeirotis, P (2010) Running experiments on Amazon Mechanical Turk. Judgment and Decision Making 5(5), 411–419.Google Scholar
Patil, AGP and Taillie, C (1982) Diversity as a concept and its measurement. Journal of the American Statistical Association 77(379), 548–561.Google Scholar
Pavoine, S, Ollier, S and Pontier, D (2005) Measuring diversity from dissimilarities with Rao's quadratic entropy: Are any dissimilarities suitable? Theoretical Population Biology 67, 231–239.Google Scholar
Perlibakas, V (2004) Distance measures for PCA-based face recognition. Pattern Recognition Letters 25, 711–724.Google Scholar
Podani, J (2009) Convex hulls, habitat filtering, and functional diversity: mathematical elegance versus ecological interpretability. Community Ecology 10(2), 244–250.Google Scholar
Rao, CR (1981) Gini-Simpson Index of Diversity: A Characterization, Generalization and Applications. Technical Report No. 81–22, University of Pittsburgh, Pittsburgh, PA.Google Scholar
Rényi, A (1961) On Measures of Entropy and Information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability 1, 547–561.Google Scholar
Risi, S, Vanderbleek, SD, Hughes, CE and Stanley, KO (2009) How Novelty Search Escapes the Deceptive Trap of Learning to Learn. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2009). New York: ACM.Google Scholar
Ritter, J (1990) An efficient bounding sphere. In Glassner, AS (ed.). Graphics Gems 1. San Diego, CA: Academic Press, pp. 301–303.Google Scholar
Robert McNeel & Associates (2014) Grasshopper. Seattle, WA: Robert McNeel & Associates. www.grasshopper3d.com.Google Scholar
Runco, MA and Charles, RE (1993) Judgments of originality and appropriateness as predictors of creativity. Personality and Individual Differences 15(5), 537–546.Google Scholar
Rusch, C (1966) The Psychological Basis for an Incremental Approach to Architecture. Berkeley, CA: University of California at Berkeley.Google Scholar
Scheibehenne, B, Greifeneder, R and Todd, PM (2010) Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research 37, 409–425.Google Scholar
Shah, J, Kulkarni, S and Vargas-Hernandez, N (2000) Guidelines for experimental evaluation of idea generation methods in conceptual design. Journal of Mechanical Design 122(4), 337–384.Google Scholar
Shah, JJ, Smith, SM and Vargas-Hernandez, N (2003) Metrics for measuring ideation effectiveness. Design Studies 24(2), 111–134.Google Scholar
Shannon, CE and Weaver, W (1949) The Mathematical Theory of Information. Urbana: University of Illinois Press.Google Scholar
Smaling, R (2005) System Architecture Analysis and Selection under Uncertainty. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
Srinivas, M and Patnaik, LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 24(4), 656–667.Google Scholar
Stump, G, Yukish, M, Simpson, T and Harris, E (2003) Design Space Visualization and Its Application to a Design by Shopping Paradigm. Proceedings of DETC’03 ASME 2003 Design Engineering Technical Conferences and Computers and Information in Engineering Conference.Google Scholar
Tsigkari, M, Chronis, A, Joyce, S, Davis, A, Feng, S and Aish, F (2013) Integrated Design in the Simulation Process. In Proceedings of the Symposium on Simulation for Architecture & Urban Design.Google Scholar
Villéger, S, Mason, NWH and Mouillot, D (2008) New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89(8), 2290–2301.Google Scholar
Wang, J, Neskovic, P and Cooper, LN (2007) Improving nearest neighbor rule with a simple adaptive distance measure. In Pattern Recognition Letters 28, 207–213.Google Scholar
Welzl, E (1991) New results and new trends in computer science: smallest enclosing disks (balls and ellipsoids). Lecture Notes in Computer Science 555, 359–370.Google Scholar
Yousif, S, Yan, W and Culp, C (2017) Incorporating form diversity into architectural design optimization. In Proceedings of ACADIA 2017: Disciplines and Disruption. Cambridge, MA.Google Scholar