Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations
- Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework,Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.…
Autor*innen: | Patrick SimonORCiD, Ronald SchneiderORCiD, Matthias BaeßlerORCiD, Christoph Recknagel |
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Persönliche Herausgeber*innen: | Á. Cunha, E. Caetano |
Dokumenttyp: | Beitrag zu einem Tagungsband |
Veröffentlichungsform: | Graue Literatur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) |
Jahr der Erstveröffentlichung: | 2021 |
Organisationseinheit der BAM: | 7 Bauwerkssicherheit |
7 Bauwerkssicherheit / 7.1 Baustoffe | |
7 Bauwerkssicherheit / 7.2 Ingenieurbau | |
Herausgeber (Institution): | International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII) |
Erste Seite: | 1537 |
Letzte Seite: | 1544 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau |
Freie Schlagwörter: | Bayesian updating; Digital twin; Material tests; Structural health monitoring |
Themenfelder/Aktivitätsfelder der BAM: | Infrastruktur |
Veranstaltung: | International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 30.06.2021 |
Enddatum der Veranstaltung: | 02.07.2021 |
ISSN: | 2564-3738 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 22.12.2021 |
Referierte Publikation: | Nein |