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Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods

  • Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makesStructural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.zeige mehrzeige weniger

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Autor*innen:Patrick SimonORCiD, Ralf HerrmannORCiD, Ronald SchneiderORCiD, Falk Hille, Matthias Baeßler, Rukeia El-AthmanORCiD
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Bridge safety, maintenance, management, life-cycle, resilience and sustainability - Proceedings of the eleventh International Conference on Bridge Maintenance, Safety and Management (IABMAS 2022), Barcelona, Spain, July 11-15, 2022
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
VP Vizepräsident
VP Vizepräsident / VP.1 eScience
Verlag:CRC Press
Verlagsort:Boca Raton
Erste Seite:1061
Letzte Seite:1068
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Artificial intelligence; Research data management; Structural health monitoring
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Veranstaltung:11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022)
Veranstaltungsort:Barcelona, Spain
Beginndatum der Veranstaltung:11.07.2022
Enddatum der Veranstaltung:15.07.2022
DOI:10.1201/9781003322641-127
ISBN:978-1-032-35623-5
ISBN:978-1-003-32264-1
Zugehöriger Identifikator:https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/55494
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:16.08.2022
Referierte Publikation:Nein
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