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dc.creatorNaranjo Pérez, Javieres
dc.creatorRodríguez Romero, Rubénes
dc.creatorPachón García, Pabloes
dc.creatorCompán Cardiel, Víctor Jesúses
dc.creatorSáez Pérez, Andréses
dc.creatorPavic, Aleksandares
dc.creatorJiménez Alonso, Javier Fernandoes
dc.date.accessioned2024-02-20T16:14:55Z
dc.date.available2024-02-20T16:14:55Z
dc.date.issued2024-04
dc.identifier.citationNaranjo Pérez, J., Rodríguez Romero, R., Pachón García, P., Compán Cardiel, V.J., Sáez Pérez, A., Pavic, A. y Jiménez Alonso, J.F. (2024). Robust improvement of the finite-element-model updating of historical constructions via a new combinative computational algorithm. Advances in Engineering Software, 190 (103598). https://doi.org/10.1016/j.advengsoft.2024.103598.
dc.identifier.issn0965-9978es
dc.identifier.urihttps://hdl.handle.net/11441/155387
dc.description.abstractFinite-element-models are usually employed to simulate the behaviour of historical constructions. However, despite the high complexity of these numerical models, there are always discrepancies between the actual behaviour of the structure and the numerical predictions obtained. In order to improve their performance, an updating process can be implemented. According to this process, the value of the most relevant physical parameters of the model is adjusted to better mimic the actual behaviour of the structure. For this purpose, the actual structural behaviour is usually characterized via its experimental modal properties (natural frequencies and associated vibration modes). For practical engineering applications, the maximum likelihood method is normally considered to cope with this problem, due to its easy implementation together with an understandable interpretation of the updating results. However, the complexity of these numerical models makes unfeasible the practical implementation of the process due to the simulation time required for its computation. In order to shed some light to this problem, a new combinative computational algorithm is proposed herein. Additionally, the performance of the proposal has been assessed successfully via two applications: (i) a validation example, the model updating of a laboratory footbridge, in which the practical implementation of the algorithm has been described in detail; and (ii) a case-study, the model updating of a complex historical construction, in which the main advantage of the proposal has been highlighted, a clear reduction of the simulation time required to solve the updating problem without compromising the accuracy of the solution obtained.es
dc.formatapplication/pdfes
dc.format.extent16 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofAdvances in Engineering Software, 190 (103598).
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFinite element model updatinges
dc.subjectMaximum likelihood methodes
dc.subjectBi-objective optimizationes
dc.subjectDecision-making problemses
dc.subjectHistorical building stonees
dc.titleRobust improvement of the finite-element-model updating of historical constructions via a new combinative computational algorithmes
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Mecánica de Medios Continuos y Teoría de Estructurases
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estructuras de Edificación e Ingeniería del Terrenoes
dc.relation.projectIDUS-1381164es
dc.relation.projectIDPID2021-127627OB-I00es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S096599782400005X?via%3Dihubes
dc.identifier.doi10.1016/j.advengsoft.2024.103598es
dc.contributor.groupUniversidad de Sevilla. TEP245: Ingeniería de las Estructurases
dc.contributor.groupUniversidad de Sevilla. TEP114: Tecnología Arquitectónicaes
dc.journaltitleAdvances in Engineering Softwarees
dc.publication.volumen190es
dc.publication.issue103598es
dc.contributor.funderAndalusian Regional Government (Spain) grant number US-1381164es
dc.contributor.funderMinisterio de Ciencia e Innovación, Spain, Agencia Estatal de Investigación, Spain and FEDER, European Union grant number PID2021-127627OB-I00es

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