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Validation and Inference of Geometrical Relationships in IFC

Authors :
Krijnen, T.F. (author)
Noardo, F. (author)
Arroyo Ohori, G.A.K. (author)
Ledoux, H. (author)
Stoter, J.E. (author)
Krijnen, T.F. (author)
Noardo, F. (author)
Arroyo Ohori, G.A.K. (author)
Ledoux, H. (author)
Stoter, J.E. (author)
Publication Year :
2020

Abstract

The Industry Foundation Classes are a prevalent open standard to exchange Building Information Models. In such a model, geometric representations are provided for individual building elements along with semantic information, including a significant amount of properties related togeometry and explicit topological relationships. These relationships and quantities introduce redundancies and often inconsistencies as well. Moreover, they introduce complexity in down-stream processing. Combining multiple aspect models into a single model has non-trivial consequences for the connectivity graphs. Programmatic mutations are complicated because of the relationships that need to be updated as a result of changes.In order to alleviate these issues, this paper provides a theoretical frameworkand implementation for both validating and inferring semantic and topological con-structs from the geometric representations, rooted on Egenhofer spatial predicates and extended with the IFC modelling tolerance. Combining these two concepts, wall connectivity is equivalent to the intersection of the wall representation boundaries, where a boundary is not a surface, but rather a hollow solid with a thickness derived from the modelling tolerance.The algorithms presented in this paper are implemented in fully open source software based on the IfcOpenShell software library and the CGAL computational geometry library using Nef polyhedra. We provide a formalization of space boundaries, spatial containment and wall connectivity relationships. The validation and inference rules are applied to a public set of building models. We conclude that exported models have geometric flaws and that several relationships can indeed be inferred by means of generic geometric intersection logic.<br />Urban Data Science

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1199588652
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.46421.2706-6568.37.2020.paper008