An Inspection of IFC Models from Practice

Journal Article (2021)
Authors

Francesca Noardo (TU Delft - Urban Data Science)

GAK Arroyo Ohori (TU Delft - Urban Data Science)

T.F. Krijnen (TU Delft - Urban Data Science)

Jantien Stoter (TU Delft - Urban Data Science)

Research Group
Urban Data Science
Copyright
© 2021 F. Noardo, G.A.K. Arroyo Ohori, T.F. Krijnen, J.E. Stoter
To reference this document use:
https://doi.org/10.3390/app11052232
More Info
expand_more
Publication Year
2021
Language
English
Copyright
© 2021 F. Noardo, G.A.K. Arroyo Ohori, T.F. Krijnen, J.E. Stoter
Research Group
Urban Data Science
Issue number
5
Volume number
11
DOI:
https://doi.org/10.3390/app11052232
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Industry Foundation Classes (IFC) is a complete, wide and complex open standard data model to represent Building Information Models. Big efforts are being made by the standardization organization buildingSMART, to develop and maintain this standard in collaboration with researchers, companies and institutions. However, when trying to use IFC models from practice for automatic analysis, some issues emerge, as a consequence of a misalignment between what is prescribed by, or available in, the standard with the data sets that are produced in practice. In this study, a sample of models produced by practitioners for aims different from their explicit use within automatic processing tools is inspected and analyzed. The aim is to find common patterns in data set from practice and their possible discrepancies with the standard, in order to find ways to address such discrepancies in a next step. In particular, it is noticeable that the overall quality of the models requires specific additional care by the modellers before relying on them for automatic analysis, and a high level of variability is present concerning the storage of some relevant information (such as georeferencing).