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Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures

Stavroulakis Georgios, Charalampidi Varvara, Koutsianitis Panagiotis

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URIhttp://purl.tuc.gr/dl/dias/303838EF-62DD-4BE7-8E89-4634758A23A7-
Identifierhttps://doi.org/10.3390/app122311997-
Identifierhttps://www.mdpi.com/2076-3417/12/23/11997-
Languageen-
Extent13 pagesen
TitleReview of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructuresen
CreatorStavroulakis Georgiosen
CreatorΣταυρουλακης Γεωργιοςel
CreatorCharalampidi Varvaraen
CreatorΧαραλαμπιδη Βαρβαραel
CreatorKoutsianitis Panagiotisen
CreatorΚουτσιανιτης Παναγιωτηςel
PublisherMDPIen
Content SummaryThis review discusses the links between the newly introduced concepts of digital twins and more classical finite element modeling, reduced order models, parametric modeling, inverse analysis, machine learning, and parameter identification. The purpose of this article is to demonstrate that development, as almost always is the case, is based on previously developed tools that are currently exploited since the technological tools for their implementation are available and the needs of their usage appear. This fact has rarely been declared clearly in the available literature. The need for digital twins in infrastructures arises due to the extreme loadings applied on energy-related infrastructure and to the higher importance that fatigue effects have. Digital twins promise to provide reliable and affordable models that accompany the structure throughout its whole lifetime, make fatigue and degradation prediction more reliable, and support effective predictive maintenance schemes.en
Type of ItemΑνασκόπησηel
Type of ItemReviewen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-08-25-
Date of Publication2022-
SubjectDigital twinsen
SubjectParametric modelingen
SubjectAnalysisen
SubjectIndustrial internet of thingsen
SubjectBig dataen
SubjectData analyticsen
SubjectArtificial intelligenceen
SubjectPredictive maintenanceen
SubjectDamage predictionen
Bibliographic CitationG. E. Stavroulakis, B. G. Charalambidi, and P. Koutsianitis, “Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures,” Appl. Sci., vol. 12, no. 23, Nov. 2022, doi: 10.3390/app122311997.en

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