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Evolutionary structural and spatial adaptation of topologically differentiated tensile systems in architectural design

  • Sean Ahlquist (a1) (a2), Dillon Erb (a1) and Achim Menges (a2)

This paper presents research in the development of heuristic evolutionary algorithms (EAs) for generating and exploring differentiated force-based structures. The algorithm is weighted toward design exploration of topological differentiation while including specific structural and material constraints. An embryological EA model is employed to “grow” networks of mass-spring elements achieving desired mesh densities that resolve themselves in tensile force (form-active) equilibrium. The primal quadrilateral quadrisection method serves as the foundation for a range of extensible subdivision methods. Unique to this research, the quad is addressed as a “cell” rather than a topological or geometric construct, allowing for the contents of the cell to vary in number of mass-spring elements and orientation. In this research, this approach has been termed the quadrilateral quadrisection with n variable topological transformation method. This research culminates with the introduction of a method for grafting meshes where emergent features from the evolved meshes can be transposed and replicated in an explicit yet informed manner. The EA and grafting methods function within a Java-based software called springFORM, developed in previous research, which utilizes a mass-spring based library for solving force equilibrium and allows for both active (manual) and algorithmic topology manipulation. In application to a specific complex tensile mesh, the design framework, which combines the generative EA and mesh grafting method, is shown to produce emergent and highly differentiated topological arrangements that negotiate the specific relationships among a desired maximal mesh density, geometric patterning, and equalized force distribution.

Corresponding author
Reprint requests to: Sean Ahlquist, Taubman College of Architecture and Urban Planning, University of Michigan, 2000 Bonisteel Boulevard, Ann Arbor, MI 48109, USA. E-mail:
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  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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