Shape Grammars: A Key Generative Design Algorithm

Ning Gu () and Peiman Amini Behbahani ()
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Ning Gu: University of South Australia, School of Art, Architecture and Design
Peiman Amini Behbahani: University of South Australia, School of Art, Architecture and Design

Chapter 52 in Handbook of the Mathematics of the Arts and Sciences, 2021, pp 1385-1405 from Springer

Abstract: Abstract Shape grammars are one of the main generative design algorithms. The theories and practices of shape grammars have developed and evolved for over four decades and showed significant impact on design computation and contemporary architecture. The formal computational approach to generative design as specified in shape grammars, and the novel descriptions and applications of shapes and shape rules for representing and composing a design, has become the foundation and inspiration for many contemporary computational design methods and tools, especially parametric design, which is a current leading computational design method. This chapter gives an overview of the historical developments and applications of shape grammars. The algorithm is introduced by highlighting the background, key components, and procedures for design generation, methods, and issues for authoring shape grammars, shape grammar evolution and extension, purposes of shape grammar application, as well as implementation of shape grammars. The characteristics of shape grammars are presented and discussed by comparing them to other key generative design algorithms, some of which have been applied in conjunction with shape grammars. This chapter shows that shape grammars have significant potentials in design generation, analysis, and optimization, as seen in many of the grammar studies. The future directions should focus on further research, improved pedagogy, as well as validation in design practice, to further advance the field.

Keywords: Shape grammars; Shape rule; Generative design algorithm; Design generation; Design analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-319-57072-3_7

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