Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange...

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Autores principales:	Yongcheng Zhang, Xuejiao Xing, Maxwell Fordjour Antwi-Afari
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	safety risk BIM IFC schema deep excavation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d91934ba90d54d67b281b1113a1f28ac
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id	oai:doaj.org-article:d91934ba90d54d67b281b1113a1f28ac
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spelling	oai:doaj.org-article:d91934ba90d54d67b281b1113a1f28ac2021-11-11T15:02:49ZSemantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects10.3390/app112199582076-3417https://doaj.org/article/d91934ba90d54d67b281b1113a1f28ac2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9958https://doaj.org/toc/2076-3417Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.Yongcheng ZhangXuejiao XingMaxwell Fordjour Antwi-AfariMDPI AGarticlesafetyriskBIMIFC schemadeep excavationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9958, p 9958 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	safety risk BIM IFC schema deep excavation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	safety risk BIM IFC schema deep excavation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
description	Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.
format	article
author	Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari
author_facet	Yongcheng Zhang Xuejiao Xing Maxwell Fordjour Antwi-Afari
author_sort	Yongcheng Zhang
title	Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
title_short	Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
title_full	Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
title_fullStr	Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
title_full_unstemmed	Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
title_sort	semantic ifc data model for automatic safety risk identification in deep excavation projects
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d91934ba90d54d67b281b1113a1f28ac
work_keys_str_mv	AT yongchengzhang semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects AT xuejiaoxing semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects AT maxwellfordjourantwiafari semanticifcdatamodelforautomaticsafetyriskidentificationindeepexcavationprojects
_version_	1718437361856020480

Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects

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