Quantifying diversity in parametric design: A comparison of possible metrics

Nathan Brown, Caitlin T. Mueller

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)40-53
Number of pages14
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume33
Issue number1
DOIs
StatePublished - Feb 1 2019

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Cite this

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Quantifying diversity in parametric design : A comparison of possible metrics. / Brown, Nathan; Mueller, Caitlin T.

In: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, Vol. 33, No. 1, 01.02.2019, p. 40-53.

Research output: Contribution to journalArticle

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