Parametric and generative methods with building information modelling : connecting BIM with explorative design modelling


Autoria(s): Fernando, Ruwan; Drogemuller, Robin; Burden, Alan
Contribuinte(s)

Kiang, Tan Beng

Data(s)

01/05/2012

Resumo

Parametric and generative modelling methods are ways in which computer models are made more flexible, and of formalising domain-specific knowledge. At present, no open standard exists for the interchange of parametric and generative information. The Industry Foundation Classes (IFC) which are an open standard for interoperability in building information models is presented as the base for an open standard in parametric modelling. The advantage of allowing parametric and generative representations are that the early design process can allow for more iteration and changes can be implemented quicker than with traditional models. This paper begins with a formal definition of what constitutes to be parametric and generative modelling methods and then proceeds to describe an open standard in which the interchange of components could be implemented. As an illustrative example of generative design, Frazer’s ‘Reptiles’ project from 1968 is reinterpreted.

Identificador

http://eprints.qut.edu.au/60213/

Publicador

Cumulative Index of Computer Aided Architectural Design

Relação

http://cumincad.scix.net/cgi-bin/works/Show?_id=caadria2012_089

Fernando, Ruwan, Drogemuller, Robin, & Burden, Alan (2012) Parametric and generative methods with building information modelling : connecting BIM with explorative design modelling. In Kiang, Tan Beng (Ed.) Beyond Codes and Pixels : CAADRIA 2012 : Proceedings of the 17th International Conference on Computer Aided Architectural Design Research in Asia, Cumulative Index of Computer Aided Architectural Design, Chennai, India, pp. 537-546.

Direitos

Copyright 2012 [please consult the author]

Fonte

School of Design; Creative Industries Faculty

Palavras-Chave #120300 DESIGN PRACTICE AND MANAGEMENT #Building Information Model #Parametric modelling #Generative modelling
Tipo

Conference Paper