Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator
Abstract Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data qualit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-11-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.12669 |
| _version_ | 1797633988298276864 |
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| author | Thomas Titscher Thomas vanDijk Daniel Kadoke Annika Robens‐Radermacher Ralf Herrmann Jörg F. Unger |
| author_facet | Thomas Titscher Thomas vanDijk Daniel Kadoke Annika Robens‐Radermacher Ralf Herrmann Jörg F. Unger |
| author_sort | Thomas Titscher |
| collection | DOAJ |
| description | Abstract Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies. |
| first_indexed | 2024-03-11T12:01:28Z |
| format | Article |
| id | doaj.art-2f9255a564ec4f1b9ca92b34c57653e1 |
| institution | Directory Open Access Journal |
| issn | 2577-8196 |
| language | English |
| last_indexed | 2024-03-11T12:01:28Z |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj.art-2f9255a564ec4f1b9ca92b34c57653e12023-11-08T01:46:20ZengWileyEngineering Reports2577-81962023-11-01511n/an/a10.1002/eng2.12669Bayesian model calibration and damage detection for a digital twin of a bridge demonstratorThomas Titscher0Thomas vanDijk1Daniel Kadoke2Annika Robens‐Radermacher3Ralf Herrmann4Jörg F. Unger5Safety of Structures Bundesanstalt für Materialforschung und‐prüfung Berlin GermanyStructural Dynamics TNO Delft NetherlandsSafety of Structures Bundesanstalt für Materialforschung und‐prüfung Berlin GermanySafety of Structures Bundesanstalt für Materialforschung und‐prüfung Berlin GermanySafety of Structures Bundesanstalt für Materialforschung und‐prüfung Berlin GermanySafety of Structures Bundesanstalt für Materialforschung und‐prüfung Berlin GermanyAbstract Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies.https://doi.org/10.1002/eng2.12669damage detectionfinite element analysisload identificationmodel updatingparameter estimationsystem identification |
| spellingShingle | Thomas Titscher Thomas vanDijk Daniel Kadoke Annika Robens‐Radermacher Ralf Herrmann Jörg F. Unger Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator Engineering Reports damage detection finite element analysis load identification model updating parameter estimation system identification |
| title | Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| title_full | Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| title_fullStr | Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| title_full_unstemmed | Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| title_short | Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| title_sort | bayesian model calibration and damage detection for a digital twin of a bridge demonstrator |
| topic | damage detection finite element analysis load identification model updating parameter estimation system identification |
| url | https://doi.org/10.1002/eng2.12669 |
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