Dezembro 2019 vol. 7 num. 1 - 37 Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1)
Article - Open Access.
Stripe Segmentation for Branching Shell Structures AData SetDevelopment as a Learning Process for Fabrication Efficiency and Structural Performance
Stripe Segmentation for Branching Shell Structures AData SetDevelopment as a Learning Process for Fabrication Efficiency and Structural Performance
Giannopoulou, Effima ; Baquero, Pablo ; Warang, Angad ; Orciuoli, Affonso ; T. Estévez, Alberto ;
Article:
This article explains the evolution towards the subject of digital fabrication ofthin shell structures, searching for the computational design techniques whichallow to implement biological pattern mechanisms for efficient fabricationprocedures. The method produces data sets in order to analyse and evaluateparallel alternatives of branching topologies, segmentation patterns, materialusage, weight and deflection values as a user learning process. The importancehere is given to the selection of the appropriate attributes, referring to whichspecific geometric characteristics of the parametric model are affecting eachother and with what impact. The outcomes are utilized to train an ArtificialNeural Network to predict new building information based on new combinationsof desired parameters so that the user can decide and adjust the design based onthe new information.
Article:
This article explains the evolution towards the subject of digital fabrication ofthin shell structures, searching for the computational design techniques whichallow to implement biological pattern mechanisms for efficient fabricationprocedures. The method produces data sets in order to analyse and evaluateparallel alternatives of branching topologies, segmentation patterns, materialusage, weight and deflection values as a user learning process. The importancehere is given to the selection of the appropriate attributes, referring to whichspecific geometric characteristics of the parametric model are affecting eachother and with what impact. The outcomes are utilized to train an ArtificialNeural Network to predict new building information based on new combinationsof desired parameters so that the user can decide and adjust the design based onthe new information.
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DOI: 10.5151/proceedings-ecaadesigradi2019_510
Referências bibliográficas
- [1] .
Como citar:
Giannopoulou, Effima; Baquero, Pablo; Warang, Angad; Orciuoli, Affonso; T. Estévez, Alberto; "Stripe Segmentation for Branching Shell Structures AData SetDevelopment as a Learning Process for Fabrication Efficiency and Structural Performance", p. 63-70 . In: Proceedings of 37 eCAADe and XXIII SIGraDi Joint Conference, “Architecture in the Age of the 4Th Industrial Revolution”, Porto 2019, Sousa, José Pedro; Henriques, Gonçalo Castro; Xavier, João Pedro (eds.).
São Paulo: Blucher,
2019.
ISSN 2318-6968,
DOI 10.5151/proceedings-ecaadesigradi2019_510
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