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Article: Automated facility inspection using robotics and BIM: A knowledge-driven approach

TitleAutomated facility inspection using robotics and BIM: A knowledge-driven approach
Authors
KeywordsBuilding information modeling (BIM)
Facility management
Inspection
Knowledge formalization
Ontology
Robotics
Issue Date2023
Citation
Advanced Engineering Informatics, 2023, article no. 101838 How to Cite?
AbstractFacility inspection is crucial for ensuring the performance of built assets. A traditional inspection, characterized by humans’ physical presence, is laborious, time-consuming, and becomes difficult to implement because of travel restrictions amid the pandemic. This laborious practice can potentially be automated by emerging smart technologies such as robotics and building information model (BIM). However, little has been known on how such automation can be achieved, concerning the knowledge-intensive nature of facility inspection. To fill the gap, this research aims to develop a knowledge-driven approach that can synergize knowledge of diverse sources (e.g., explicit knowledge from BIM, and tacit experience in human minds) to allow autonomous implementation of facility inspection by robotic agents. At the core the approach is an integrated scene-task-agent (iSTA) model that formalizes engineering priori in facility management and integrates the rich contextual information from BIM. Experiments demonstrated the applicability of the approach, which can endow robots with autonomy and knowledge to navigate the challenging built environments and deliver facility inspection outcomes. The iSTA model is publicized online, in hope of further extension by the research community and practical deployment to enable automated facility inspection using robotics and BIM.
Persistent Identifierhttp://hdl.handle.net/10722/323592
ISSN
2023 Impact Factor: 8.0
2023 SCImago Journal Rankings: 1.731
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, J-
dc.contributor.authorLu, WW-
dc.contributor.authorFu, Y-
dc.contributor.authorDONG, Z-
dc.date.accessioned2023-01-08T07:09:01Z-
dc.date.available2023-01-08T07:09:01Z-
dc.date.issued2023-
dc.identifier.citationAdvanced Engineering Informatics, 2023, article no. 101838-
dc.identifier.issn1474-0346-
dc.identifier.urihttp://hdl.handle.net/10722/323592-
dc.description.abstractFacility inspection is crucial for ensuring the performance of built assets. A traditional inspection, characterized by humans’ physical presence, is laborious, time-consuming, and becomes difficult to implement because of travel restrictions amid the pandemic. This laborious practice can potentially be automated by emerging smart technologies such as robotics and building information model (BIM). However, little has been known on how such automation can be achieved, concerning the knowledge-intensive nature of facility inspection. To fill the gap, this research aims to develop a knowledge-driven approach that can synergize knowledge of diverse sources (e.g., explicit knowledge from BIM, and tacit experience in human minds) to allow autonomous implementation of facility inspection by robotic agents. At the core the approach is an integrated scene-task-agent (iSTA) model that formalizes engineering priori in facility management and integrates the rich contextual information from BIM. Experiments demonstrated the applicability of the approach, which can endow robots with autonomy and knowledge to navigate the challenging built environments and deliver facility inspection outcomes. The iSTA model is publicized online, in hope of further extension by the research community and practical deployment to enable automated facility inspection using robotics and BIM.-
dc.languageeng-
dc.relation.ispartofAdvanced Engineering Informatics-
dc.subjectBuilding information modeling (BIM)-
dc.subjectFacility management-
dc.subjectInspection-
dc.subjectKnowledge formalization-
dc.subjectOntology-
dc.subjectRobotics-
dc.titleAutomated facility inspection using robotics and BIM: A knowledge-driven approach-
dc.typeArticle-
dc.identifier.emailChen, J: chenjj10@hku.hk-
dc.identifier.emailLu, WW: wilsonlu@hku.hk-
dc.identifier.emailFu, Y: fyongl@hku.hk-
dc.identifier.authorityChen, J=rp03048-
dc.identifier.authorityLu, WW=rp01362-
dc.identifier.doi10.1016/j.aei.2022.101838-
dc.identifier.scopuseid_2-s2.0-85144052761-
dc.identifier.hkuros343250-
dc.identifier.spagearticle no. 101838-
dc.identifier.epagearticle no. 101838-
dc.identifier.isiWOS:000895072000002-

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