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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

2020-06Journal article Open access
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dc.contributor.authorFerreira, Tiago Miguel
dc.contributor.authorEstêvão, João M. C.
dc.contributor.authorMaio, Rui
dc.contributor.authorVicente, R.
dc.date.accessioned2020-07-22T08:48:45Z
dc.date.available2020-07-22T08:48:45Z
dc.date.issued2020-06
dc.description.abstractThis paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.pt_PT
dc.description.sponsorshipSFRH/BPD/122598/2016pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFerreira, Tiago Miguel; Estêvão, João; Maio, Rui; Vicente, Romeu. "The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry". Frontiers of Structural and Civil Engineering 14 3 (2020): 609-622. http://dx.doi.org/10.1007/s11709-020-0623-6pt_PT
dc.identifier.doi10.1007/s11709-020-0623-6pt_PT
dc.identifier.issn2095-2449
dc.identifier.urihttp://hdl.handle.net/10400.1/14108
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial Neural Networkspt_PT
dc.subjectseismic vulnerabilitypt_PT
dc.subjectmasonry buildingspt_PT
dc.subjectdamage estimationpt_PT
dc.subjectvulnerability curvespt_PT
dc.titleThe use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonrypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage622pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage609pt_PT
oaire.citation.titleFrontiers of Structural and Civil Engineeringpt_PT
oaire.citation.volume14pt_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.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication39e5f28b-fdf6-4823-b622-87f4177dd013
relation.isAuthorOfPublication.latestForDiscovery39e5f28b-fdf6-4823-b622-87f4177dd013

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