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dc.contributor.authorStorni, Daniele
dc.contributor.authorSingh, Harpal
dc.contributor.authorTangrand, Kristoffer Meyer
dc.contributor.authorGrip, Niklas
dc.date.accessioned2025-01-02T11:57:36Z
dc.date.available2025-01-02T11:57:36Z
dc.date.issued2024-02-23
dc.description.abstractIn this paper, we present a comprehensive study of wavelet theory with a focus on structural damage detection. A new example of the application of operational modal analysis (OMA) techniques to a concrete railway arch bridge located over the Kalix river in L{\aa}ngforsen, Sweden is presented. Results from the OMA techniques are used for finite element model (FEM) updating of this concrete railway arch bridge. Further, a new case study for sensor placement on Her{\o}ysund bridge located in Nordland, Norway to conduct OMA is discussed in detail. Moreover, artificial intelligence algorithms that can be useful for addressing the problem of missing data sets in structural health monitoring technologies are reviewed.en_US
dc.descriptionSource at <a href=https://nonlinearstudies.com/index.php/mesa/article/view/3529>https://nonlinearstudies.com/index.php/mesa/article/view/3529</a>.en_US
dc.identifier.citationStorni, Singh, Tangrand, Grip. Research on wavelets and artificial intelligence algorithms for structural health monitoring of concrete bridges. Mathematics in Engineering, Science and Aerospace (MESA). 2024;15(1):131-150en_US
dc.identifier.cristinIDFRIDAID 2308957
dc.identifier.issn2041-3165
dc.identifier.issn2041-3173
dc.identifier.urihttps://hdl.handle.net/10037/36068
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalMathematics in Engineering, Science and Aerospace (MESA)
dc.relation.urihttps://nonlinearstudies.com/index.php/mesa/article/view/3529
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.titleResearch on wavelets and artificial intelligence algorithms for structural health monitoring of concrete bridgesen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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