A Bayesian Framework for Simulation‐based Digital Twins of Bridges Article Swipe
Daniel Andrés Arcones
,
Martin Weiser
,
Phaedon‐Stelios Koutsourelakis
,
Jörg F. Unger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/cepa.2177
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/cepa.2177
Simulation‐based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their structural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation‐based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up‐to‐date representation of bridge behavior, helping to inform decision‐making for maintenance and management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cepa.2177
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cepa.2177
- OA Status
- hybrid
- Cited By
- 6
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387025833
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387025833Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/cepa.2177Digital Object Identifier
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A Bayesian Framework for Simulation‐based Digital Twins of BridgesWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-09-01Full publication date if available
- Authors
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Daniel Andrés Arcones, Martin Weiser, Phaedon‐Stelios Koutsourelakis, Jörg F. UngerList of authors in order
- Landing page
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https://doi.org/10.1002/cepa.2177Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cepa.2177Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cepa.2177Direct OA link when available
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Bridge (graph theory), Computer science, Context (archaeology), Representation (politics), Virtual representation, Bayesian probability, Artificial intelligence, Biology, Politics, Political science, Internal medicine, Law, Medicine, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.understanding | 53 |
| abstract_inverted_index.implementation | 134 |
| abstract_inverted_index.representation | 151 |
| abstract_inverted_index.uncertainties. | 131 |
| abstract_inverted_index.up‐to‐date | 150 |
| abstract_inverted_index.characteristics | 76 |
| abstract_inverted_index.representations | 19 |
| abstract_inverted_index.decision‐making | 158 |
| abstract_inverted_index.Simulation‐based | 1 |
| abstract_inverted_index.simulation‐based | 107 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5035638107 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.74497904 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |