The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/14108 |
Resumo: | This 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. |
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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonryArtificial Neural Networksseismic vulnerabilitymasonry buildingsdamage estimationvulnerability curvesThis 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.SFRH/BPD/122598/2016SpringerSapientiaFerreira, Tiago MiguelEstêvão, João M. C.Maio, RuiVicente, R.2020-07-22T08:48:45Z2020-062020-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/14108engFerreira, 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/s11709-020-0623-62095-244910.1007/s11709-020-0623-6info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:26:21Zoai:sapientia.ualg.pt:10400.1/14108Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:05:09.610020Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
title |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
spellingShingle |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry Ferreira, Tiago Miguel Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves |
title_short |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
title_full |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
title_fullStr |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
title_full_unstemmed |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
title_sort |
The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry |
author |
Ferreira, Tiago Miguel |
author_facet |
Ferreira, Tiago Miguel Estêvão, João M. C. Maio, Rui Vicente, R. |
author_role |
author |
author2 |
Estêvão, João M. C. Maio, Rui Vicente, R. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Ferreira, Tiago Miguel Estêvão, João M. C. Maio, Rui Vicente, R. |
dc.subject.por.fl_str_mv |
Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves |
topic |
Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves |
description |
This 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. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-07-22T08:48:45Z 2020-06 2020-06-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/14108 |
url |
http://hdl.handle.net/10400.1/14108 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ferreira, 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/s11709-020-0623-6 2095-2449 10.1007/s11709-020-0623-6 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133290022567936 |