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Building Model Object Classification for Semantic Enrichment Using Geometric Features and Pairwise Spatial Relations

Ma, Ling, Sacks, Rafael and Kattell, Uri (2017) Building Model Object Classification for Semantic Enrichment Using Geometric Features and Pairwise Spatial Relations. In: 2017 Lean and Computing in Construction Congress (LC3), July 4-7, 2017, Heraklion, Crete, Greece.

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Abstract

Semantic enrichment is a process of supplementing/correcting information in a poorly prepared BIM model. Object classifications are essential information, but are commonly missing or incorrectly represented when transferring a BIM model or creating a model using tools customized for other domains in design. Automated compilation of 'as-is' BIM models from point cloud data also requires object classification, as well as 3D reconstruction. We present a systematic approach to classifying objects in a BIM model, for use in future semantic enrichment systems. Previous work on object classification in BIM model enrichment was restricted by its limited ability to accurately interpret geometric and spatial features and by the constraints of Boolean logic rules and the rule compilation process. To address these issues, we propose a procedure for establishing a knowledge base that associates objects with their features and relationships, and a matching algorithm based on a similarity measurement between the knowledge base and facts. An implementation on a synthetic bridge model shows that whereas some objects can be classified by shape features alone, most objects require the use of spatial relations for unique classification. Spatial context is more likely to uniquely identify an object than shape features are.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Building information modelling; Semantic enrichment; Geometric feature; Spatial relation; Object classification
Subjects: T Technology > TH Building construction
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Jonathan Cook
Date Deposited: 08 Sep 2017 12:13
Last Modified: 08 Sep 2017 21:07
URI: http://eprints.hud.ac.uk/id/eprint/33185

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