The STG-framework: a pattern-based algorithmic framework for developing generative models of parametric architectural design at the conceptual design stage

ABSTRACT

Although algorithmic modeling tools have become a popular means of generating complex geometric forms, the potential of generative algorithms should not be limited to geometric intentions. However, the need to possess programming and data manipulation skills is often a major obstacle when architects wish to implement algorithms for representing their non-geometric intentions. This paper therefore proposes an algorithmic framework entitled STGf, which is based on the “Semantic-Topological-Geometric (STG)” information conversion pattern, and can help architects to convert their abstract design intentions into computational procedures. By providing rewritable sample GhPython scripts and adjustable components’ clusters of Grasshopper, the STGf framework aims to help architects for representing then to explore their abstract intentions beyond geometric features at an early design stage.

GRAPHICAL ABSTRACT

Keywords:

• Design intention
• parametric design
• generative modeling
• design pattern
• algorithmic framework

Acknowledgements

The Ministry of Science and Technology of Taiwan supported this paper under grant number MOST 105-2221-E-165-002.

ORCID

Chieh-Jen Lin http://orcid.org/0000-0001-9981-5864