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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

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/303838EF-62DD-4BE7-8E89-4634758A23A7-
Αναγνωριστικόhttps://doi.org/10.3390/app122311997-
Αναγνωριστικόhttps://www.mdpi.com/2076-3417/12/23/11997-
Γλώσσαen-
Μέγεθος13 pagesen
ΤίτλοςReview of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructuresen
ΔημιουργόςStavroulakis Georgiosen
ΔημιουργόςΣταυρουλακης Γεωργιοςel
ΔημιουργόςCharalampidi Varvaraen
ΔημιουργόςΧαραλαμπιδη Βαρβαραel
ΔημιουργόςKoutsianitis Panagiotisen
ΔημιουργόςΚουτσιανιτης Παναγιωτηςel
ΕκδότηςMDPIen
ΠερίληψηThis 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
ΤύποςΑνασκόπησηel
ΤύποςReviewen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-08-25-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαDigital twinsen
Θεματική ΚατηγορίαParametric modelingen
Θεματική ΚατηγορίαAnalysisen
Θεματική ΚατηγορίαIndustrial internet of thingsen
Θεματική ΚατηγορίαBig dataen
Θεματική ΚατηγορίαData analyticsen
Θεματική ΚατηγορίαArtificial intelligenceen
Θεματική ΚατηγορίαPredictive maintenanceen
Θεματική ΚατηγορίαDamage predictionen
Βιβλιογραφική ΑναφοράG. 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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