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Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks

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Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks

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dc.contributor.author Makoond, Nirvan Chandra es_ES
dc.contributor.author Pelà, Luca es_ES
dc.contributor.author Molins, Climent es_ES
dc.date.accessioned 2023-10-19T18:02:02Z
dc.date.available 2023-10-19T18:02:02Z
dc.date.issued 2023-07 es_ES
dc.identifier.issn 1475-9217 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198421
dc.description.abstract [EN] Widely used simplified analytical methods for estimating the tensile force in tie-rods are clearly not applicable when they contain significant discontinuities or irregularities. A common example for which this fact becomes relevant in practice is the use of connectors to unify historical ties consisting of several segments. To address this challenge, a robust hybrid methodology is proposed which can be applied to any historical tie by employing a data-driven approach to a dataset generated using the finite element method. The methodology is applied to a real case study involving two historical ties. es_ES
dc.description.sponsorship The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Servei del Patrimoni Arquitectònic of the Generalitat de Catalunya through a project (managed by the City Council of Sant Cugat) aimed at monitoring the church of the Monastery of Sant Cugat (grant number C-10764); the Ministry of Science, Innovation and Universities of the Spanish Government and the ERDF (European Regional Development Fund) through the SEVERUS project (Multilevel evaluation of seismic vulnerability and risk mitigation of masonry buildings in resilient historical urban centres) (grant number RTI2018-099589-B-I00); and the AGAUR agency of the Generalitat de Catalunya in the form of the predoctoral grant of the first author. es_ES
dc.language Inglés es_ES
dc.publisher SAGE Publications es_ES
dc.relation.ispartof Structural Health Monitoring es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Axial force estimation es_ES
dc.subject Vibration testing es_ES
dc.subject Dynamic identification es_ES
dc.subject Modal analysis es_ES
dc.subject Natural frequency es_ES
dc.subject Mode shapes es_ES
dc.title Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1177/14759217221123326 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099589-B-I00/ES/EVALUACION MULTINIVEL DE LA VULNERABILIDADD SISMICA Y MITIGACION DE RIESGO DE EDIFICIOS DE OBRA DE FABRICA EN CENTROS URBANOS HISTORICOS RESILIENTES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GC//C-10764//Servei del Patrimoni Arquitectònic/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Makoond, NC.; Pelà, L.; Molins, C. (2023). Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks. Structural Health Monitoring. 22(4):2496-2515. https://doi.org/10.1177/14759217221123326 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1177/14759217221123326 es_ES
dc.description.upvformatpinicio 2496 es_ES
dc.description.upvformatpfin 2515 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\486293 es_ES
dc.contributor.funder Generalitat de Catalunya es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Agencia de Gestión de Ayudas Universitarias y de Investigación es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES
dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


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