The ISO-registered industry foundation classes (IFC) data standard can facilitate building information modeling (BIM) interoperability by allowing a “one-to-many” software information flow pattern in the architecture, engineering, and construction (AEC) industry instead of the more complex “many-to-many” information flow pattern. However, even a full adoption of IFC cannot guarantee a seamless BIM interoperability, because of the flexibility allowed in adopting/using the IFC standard. Object classification in BIM is an important part of BIM interoperability. As part of an initial effort to pursue seamless BIM interoperability, the authors propose to use intrinsic geometric representations of IFC objects and geometric theorems to support the automated BIM object classification. A six-step method was proposed to develop automated IFC object classification algorithms using a data-driven approach. The method was preliminarily tested on classifying IFC objects into a cone frustum shape. The developed algorithm was successfully tested on three unseen bridge data. This shows an early promise of the proposed method to support error-free BIM object classification.
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Introducing Geometric Signatures of Architecture, Engineering, and Construction Objects and a New BIM Dataset
Object signatures have been widely used in object detection and classification. Following a similar idea, the authors developed geometric signatures for architecture, engineering, and construction (AEC) objects such as footings, slabs, walls, beams, and columns. The signatures were developed both scientifically and empirically, by following a data-driven approach based on analysis of collected building information modeling (BIM) data using geometric theories. Rigorous geometric properties and statistical information were included in the developed geometric signatures. To enable an open access to BIM data using these signatures, the authors also initiated a BIM data repository with a preliminary collection of AEC objects and their geometric signatures. The developed geometric signatures were preliminarily tested by a small object classification experiment where 389 object instances from an architectural model were used. A rule-based algorithm developed using all parameter values of 14 features from the geometric signatures of the objects successfully classified 336 object instances into the correct categories of beams, columns, slabs, and walls. This higher than 85% accuracy showed the developed geometric signatures are promising. The collected and processed data were deposited into the Purdue University Research Repository (PURR) for sharing.
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- Award ID(s):
- 1745374
- NSF-PAR ID:
- 10104659
- Date Published:
- Journal Name:
- ASCE International Conference on Computing in Civil Engineering 2019
- Page Range / eLocation ID:
- 264 to 271
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482421.034
