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Parametric Design: Theoretical Development and Algorithmic Foundation for Design Generation in Architecture

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Handbook of the Mathematics of the Arts and Sciences

Abstract

This chapter presents the theoretical foundation of parametric design for design generation in architecture. Parametric design has been increasingly applied to architectural design in recent years. It is essentially a digital design method, which can be characterized by rule-algorithmic design and multiple-solution generation. Parametric design originates from generative design, which is a typical computational design approach based on rules or algorithms (e.g., in generative grammars or evolutionary systems). This chapter starts with a critical review of generative design, followed by the background, history, and theory of parametric design, including various fundamental concepts and applications that underpin parametric design, and concludes with a discussion of the impact of parametric design on architecture.

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Authors and Affiliations

  1. School of Art, Architecture and Design, University of South Australia, Adelaide, Australia

    Ning Gu & Peiman Amini Behbahani

  2. School of Engineering and Built Environment, Griffith University, Southport, Australia

    Rongrong Yu

Authors
  1. Ning Gu
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  2. Rongrong Yu
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  3. Peiman Amini Behbahani
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Correspondence to Ning Gu .

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Editors and Affiliations

  1. Dept of Mathematical Sciences, The University of Montana Dept of Mathematical Sciences, Missoula, Montana, USA

    Bharath Sriraman

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Gu, N., Yu, R., Behbahani, P.A. (2018). Parametric Design: Theoretical Development and Algorithmic Foundation for Design Generation in Architecture. In: Sriraman, B. (eds) Handbook of the Mathematics of the Arts and Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-70658-0_8-1

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  • DOI: https://doi.org/10.1007/978-3-319-70658-0_8-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70658-0

  • Online ISBN: 978-3-319-70658-0

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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