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.…
Autor*innen: | Patrick SimonORCiD |
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Weitere Person: | Ralf HerrmannORCiD, Ronald SchneiderORCiD, Falk HilleORCiD, Matthias BaeßlerORCiD, Rukeia El-Athman |
Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2022 |
Organisationseinheit der BAM: | 7 Bauwerkssicherheit |
7 Bauwerkssicherheit / 7.2 Ingenieurbau | |
VP Vizepräsident | |
VP Vizepräsident / VP.1 eScience | |
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 |
Zugehöriger Identifikator: | https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/55493 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 16.08.2022 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |