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The use of artificial neural networks to estimate seismic damage in traditional masonry buildings

dc.contributor.authorFerreira, Tiago Miguel
dc.contributor.authorEstêvão, João Manuel Carvalho
dc.contributor.authorMaio, Rui
dc.contributor.authorVicente, Romeu
dc.date.accessioned2018-06-25T10:07:52Z
dc.date.available2018-06-25T10:07:52Z
dc.date.issued2018-06
dc.description.abstractThe present paper aims to discuss alternative strategies to estimate earthquake damage inflicted to traditional masonry buildings through a comparative analysis of the results obtained resorting to two different approaches: a seismic vulnerability index scoring method and physical damage estimation, widely used in the past in numerous large-scale earthquake risk assessment studies, and an innovative approach based on the use of Artificial Neural Networks. The post-earthquake damage data collected in the aftermath of the magnitude VII earthquake that struck the Azores archipelago (in Portugal) on July 9, 1998, was used to generate real damage data for a set of traditional masonry buildings located in the island of Faial. This data was then compared to the analytical results obtained through the referred approaches for different macroseismic intensities, IEMS-98. Finally, the fitting of the mean damage grade values estimated by the scoring method and calculated through the artificial neural network are compared and critically discussed.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationT. M. Ferreira, J. M. C. Estêvão, R. Maio, R. Vicente, The use of artificial neural networks to estimate seismic damage in traditional masonry buildings, In: Proceedings of 16th European Conference on Earthquake Engineering (16ECEE), Thessaloniki, Greece, 2018, pp. 1-10, paper 10827.pt_PT
dc.identifier.otherAUT: JES00807;
dc.identifier.urihttp://hdl.handle.net/10400.1/10716
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectSeismic vulnerabilitypt_PT
dc.subjectDamage estimationpt_PT
dc.subjectMasonry buildingspt_PT
dc.subjectIndex-based approachpt_PT
dc.subjectArtificial neural networkspt_PT
dc.titleThe use of artificial neural networks to estimate seismic damage in traditional masonry buildingspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceThessaloniki, Greecept_PT
oaire.citation.endPage10pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title16th European Conference on Earthquake Engineering (16ECEE)pt_PT
person.familyNameEstêvão
person.givenNameJoão Manuel Carvalho
person.identifierLh0jYe0AAAAJ&hl
person.identifier.ciencia-id001A-8761-A164
person.identifier.orcid0000-0002-7356-9893
person.identifier.scopus-author-id56268965500
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication39e5f28b-fdf6-4823-b622-87f4177dd013
relation.isAuthorOfPublication.latestForDiscovery39e5f28b-fdf6-4823-b622-87f4177dd013

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