4.6 Article

An Inspection of IFC Models from Practice

期刊

APPLIED SCIENCES-BASEL
卷 11, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/app11052232

关键词

Building Information Modeling; BIM; Industry Foundation Classes; IFC; interoperability; modelling practice

资金

  1. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [707404]
  2. European Research Council (ERC) under the European Union's Horizon2020 Research & Innovation Programme [677312]

向作者/读者索取更多资源

IFC is a comprehensive open standard data model used to represent Building Information Models, but issues can arise when trying to use IFC models from practice for automatic analysis due to misalignment between the standard and the data sets produced in practice. This study aims to identify common patterns in data sets from practice and their discrepancies with the standard in order to address such discrepancies.
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).

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