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
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