Automated structural defects diagnosis in underground transportation tunnels using semantic technologies

Hu, M; Liu, Y; Sugumaran, V; Liu, B; Du, J

Automated structural defects diagnosis in underground transportation tunnels using semantic technologies

Hu, M Liu, Y Sugumaran, V Liu, B Du, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Automation in Construction, 2019, 107
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Hu, M
dc.contributor.author	Liu, Y
dc.contributor.author	Sugumaran, V
dc.contributor.author	Liu, B
dc.contributor.author	Du, J https://orcid.org/0000-0002-6428-7763
dc.date.accessioned	2022-07-06T04:26:12Z
dc.date.available	2022-07-06T04:26:12Z
dc.date.issued	2019-11-01
dc.identifier.citation	Automation in Construction, 2019, 107
dc.identifier.issn	0926-5805
dc.identifier.issn	1872-7891
dc.identifier.uri	http://hdl.handle.net/10453/158677
dc.description.abstract	Detecting structural defects and finding the underlying causes accurately and timely is crucial for developing effective maintenance strategies to keep up tunnel safety and availability. Data-driven methods are gradually becoming the norm for tunnel health diagnosis but data involved are usually limited to a single system or a particular type of defect. These methods are not effective for cross-system or multi-stage detection because of the following difficulties: (1) high data heterogeneity, (2) complex spatiotemporal relationships, and (3) high expert knowledge involvement required. In order to overcome these challenges, this paper follows the constructive research approach to develop a system called Tunnel Defects Diagnosis System (TDDS) based on Industry Foundation Classes (IFC) and Semantic Web technologies. In TDDS, a meta-standard is introduced to set up mapping rules for the integration of heterogeneous data and a Tunnel Diagnosis Ontology (TDO) is established to formally define the complex spatiotemporal relationships among data. Predefined rules based on expert knowledge enable automatic reasoning to provide support for decision making with respect to cause detection. This system has been applied to the Dalian Road Tunnel in Shanghai to demonstrate its feasibility and effectiveness.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Automation in Construction
dc.relation.isbasedon	10.1016/j.autcon.2019.102929
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering, 12 Built Environment and Design
dc.subject.classification	Building & Construction
dc.title	Automated structural defects diagnosis in underground transportation tunnels using semantic technologies
dc.type	Journal Article
utslib.citation.volume	107
utslib.for	09 Engineering
utslib.for	12 Built Environment and Design
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-06T04:25:25Z
pubs.publication-status	Published
pubs.volume	107

Abstract:

Detecting structural defects and finding the underlying causes accurately and timely is crucial for developing effective maintenance strategies to keep up tunnel safety and availability. Data-driven methods are gradually becoming the norm for tunnel health diagnosis but data involved are usually limited to a single system or a particular type of defect. These methods are not effective for cross-system or multi-stage detection because of the following difficulties: (1) high data heterogeneity, (2) complex spatiotemporal relationships, and (3) high expert knowledge involvement required. In order to overcome these challenges, this paper follows the constructive research approach to develop a system called Tunnel Defects Diagnosis System (TDDS) based on Industry Foundation Classes (IFC) and Semantic Web technologies. In TDDS, a meta-standard is introduced to set up mapping rules for the integration of heterogeneous data and a Tunnel Diagnosis Ontology (TDO) is established to formally define the complex spatiotemporal relationships among data. Predefined rules based on expert knowledge enable automatic reasoning to provide support for decision making with respect to cause detection. This system has been applied to the Dalian Road Tunnel in Shanghai to demonstrate its feasibility and effectiveness.

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http://hdl.handle.net/10453/158677