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Breaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence

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dc.contributor.authorLoy-Benitez, Jorge-
dc.contributor.authorSong, Myung Kyu-
dc.contributor.authorChoi, Yo-Hyun-
dc.contributor.authorLee, Je-Kyum-
dc.contributor.authorLee, Sean Seungwon-
dc.date.accessioned2023-12-11T07:31:43Z-
dc.date.available2023-12-11T07:31:43Z-
dc.date.issued2024-02-
dc.identifier.issn0926-5805-
dc.identifier.issn1872-7891-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/193255-
dc.description.abstractAdvances in tunnel boring machines (TBM) have leveraged applied artificial intelligence to promote sustainable and automatic tunneling construction. This paper highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent key contributions in the field by identifying three key parallel subsystems: modeling, monitoring, and control. Moreover, each subsystem is evaluated from the standpoint of practical implications. In this context, specific challenges are identified, suggesting research paths that include integrated management systems, and encouraging further investigations into TBM automation by integrating the existing management subsystems from an operational perspective.-
dc.format.extent23-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier BV-
dc.titleBreaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.autcon.2023.105199-
dc.identifier.scopusid2-s2.0-85177843947-
dc.identifier.wosid001125026300001-
dc.identifier.bibliographicCitationAutomation in Construction, v.158, pp 1 - 23-
dc.citation.titleAutomation in Construction-
dc.citation.volume158-
dc.citation.startPage1-
dc.citation.endPage23-
dc.type.docTypeReview-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaConstruction & Building Technology-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryConstruction & Building Technology-
dc.relation.journalWebOfScienceCategoryEngineering, Civil-
dc.subject.keywordPlusINDOOR AIR-QUALITY-
dc.subject.keywordPlusVARIATIONAL AUTOENCODER-
dc.subject.keywordPlusSENSOR VALIDATION-
dc.subject.keywordPlusFAULT-DETECTION-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusPREDICTION-
dc.subject.keywordPlusDIAGNOSIS-
dc.subject.keywordPlusENERGY-
dc.subject.keywordPlusRECONSTRUCTION-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorAutomatic tunneling-
dc.subject.keywordAuthorIntegrated management systems-
dc.subject.keywordAuthorSmart infrastructure management-
dc.subject.keywordAuthorTunnel boring machine-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0926580523004594?via%3Dihub-
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