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Article: Automatic detection of geometric errors in space boundaries of IFC-BIM models using a Monte Carlo ray tracing approach

TitleAutomatic detection of geometric errors in space boundaries of IFC-BIM models using a Monte Carlo ray tracing approach
Authors
KeywordsAxis-aligned bounding box (AABB) tree
Geometric errors
Industry Foundation Classes (IFC)
Monte Carlo method
Ray tracing
Space boundary
Issue Date2020
PublisherAmerican Society of Civil Engineers.
Citation
Journal of Computing in Civil Engineering, 2020, v. 34 n. 2 How to Cite?
AbstractIn the Industry Foundation Classes (IFC), the objectified concept of space boundary (SB) provides a means to define building space geometries with surface entities. Such building geometry definitions are widely used for various engineering applications such as energy simulation, lighting analysis and facility management. However, the quality issues (i.e., geometric and non-geometric issues) of SBs have been widely reported, which makes it necessary to validate the SBs before retrieving them from IFC models for relevant applications. Unfortunately, there is still a lack of reliable mechanisms/tools to automatically evaluate the quality of SBs, especially the geometric quality. This study proposes a Monte Carlo ray tracing approach to automatically detect geometric errors in SBs. The approach checks SBs space-by-space whether each space is correctly bounded by its SBs. The geometric errors in the set of SBs of a space that the approach can detect include gaps, overhangs, and overlaps between SBs as well as incorrect surface normal directions of SBs. To accelerate the ray tracing process in the approach, the axis-aligned bounding box (AABB) tree is implemented to spatially index SBs of each space. The approach is evaluated with extensive performance tests in terms of robustness and efficiency. The results show that the approach can robustly and efficiently detect all the four types of geometric errors even in extreme cases and that the AABB tree helps speed up the approach significantly for large-scale IFC models with many complex spaces.
Persistent Identifierhttp://hdl.handle.net/10722/274601
ISSN
2020 Impact Factor: 4.64
2020 SCImago Journal Rankings: 0.936
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYing, H-
dc.contributor.authorLee, SH-
dc.date.accessioned2019-08-18T15:05:05Z-
dc.date.available2019-08-18T15:05:05Z-
dc.date.issued2020-
dc.identifier.citationJournal of Computing in Civil Engineering, 2020, v. 34 n. 2-
dc.identifier.issn0887-3801-
dc.identifier.urihttp://hdl.handle.net/10722/274601-
dc.description.abstractIn the Industry Foundation Classes (IFC), the objectified concept of space boundary (SB) provides a means to define building space geometries with surface entities. Such building geometry definitions are widely used for various engineering applications such as energy simulation, lighting analysis and facility management. However, the quality issues (i.e., geometric and non-geometric issues) of SBs have been widely reported, which makes it necessary to validate the SBs before retrieving them from IFC models for relevant applications. Unfortunately, there is still a lack of reliable mechanisms/tools to automatically evaluate the quality of SBs, especially the geometric quality. This study proposes a Monte Carlo ray tracing approach to automatically detect geometric errors in SBs. The approach checks SBs space-by-space whether each space is correctly bounded by its SBs. The geometric errors in the set of SBs of a space that the approach can detect include gaps, overhangs, and overlaps between SBs as well as incorrect surface normal directions of SBs. To accelerate the ray tracing process in the approach, the axis-aligned bounding box (AABB) tree is implemented to spatially index SBs of each space. The approach is evaluated with extensive performance tests in terms of robustness and efficiency. The results show that the approach can robustly and efficiently detect all the four types of geometric errors even in extreme cases and that the AABB tree helps speed up the approach significantly for large-scale IFC models with many complex spaces.-
dc.languageeng-
dc.publisherAmerican Society of Civil Engineers.-
dc.relation.ispartofJournal of Computing in Civil Engineering-
dc.rightsJournal of Computing in Civil Engineering. Copyright © American Society of Civil Engineers.-
dc.rightsThis material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at [URL/link of abstract in the ASCE Library or Civil Engineering Database].-
dc.subjectAxis-aligned bounding box (AABB) tree-
dc.subjectGeometric errors-
dc.subjectIndustry Foundation Classes (IFC)-
dc.subjectMonte Carlo method-
dc.subjectRay tracing-
dc.subjectSpace boundary-
dc.titleAutomatic detection of geometric errors in space boundaries of IFC-BIM models using a Monte Carlo ray tracing approach-
dc.typeArticle-
dc.identifier.emailLee, SH: shlee1@hku.hk-
dc.identifier.authorityLee, SH=rp01910-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1061/(ASCE)CP.1943-5487.0000878-
dc.identifier.scopuseid_2-s2.0-85077190301-
dc.identifier.hkuros301879-
dc.identifier.volume34-
dc.identifier.issue2-
dc.identifier.isiWOS:000508188100003-
dc.publisher.placeUnited States-
dc.identifier.issnl0887-3801-

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