Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/88552 |
Resumo: | In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations. |
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Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakesGeographical Information Systemadobedamage databasefield surveymachine learningseismic damagesseismic vulnerability assessmentEngenharia e Tecnologia::Engenharia CivilArts & HumanitiesScience & TechnologyIn September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations.- This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R & D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020. This research was funded by the Portuguese Foundation for Science and Technology (FCT) through grant number PD/BD/150385/2019. The field campaigns in the State of Morelos were financed by the Instituto de Ingenieria - Universidad Nacional Autonoma de Mexico (Institute of Engineering - National Autonomous University of Mexico) through the project R562.Taylor & FrancisUniversidade do MinhoEudave, Rafael RamirezFerreira, Tiago MiguelVicente, RomeuLourenço, Paulo B.Pena, Fernando2023-042023-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88552engRamírez Eudave, R., Ferreira, T. M., Vicente, R., Lourenco, P. B., & Peña, F. (2023, April 16). Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes. International Journal of Architectural Heritage. Informa UK Limited. http://doi.org/10.1080/15583058.2023.22007391558-305810.1080/15583058.2023.2200739https://www.tandfonline.com/doi/full/10.1080/15583058.2023.2200739info: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:RCAAP2024-02-10T01:19:48Zoai:repositorium.sdum.uminho.pt:1822/88552Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:37:07.803726Repositório 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 |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
title |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
spellingShingle |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes Eudave, Rafael Ramirez Geographical Information System adobe damage database field survey machine learning seismic damages seismic vulnerability assessment Engenharia e Tecnologia::Engenharia Civil Arts & Humanities Science & Technology |
title_short |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
title_full |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
title_fullStr |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
title_full_unstemmed |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
title_sort |
Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes |
author |
Eudave, Rafael Ramirez |
author_facet |
Eudave, Rafael Ramirez Ferreira, Tiago Miguel Vicente, Romeu Lourenço, Paulo B. Pena, Fernando |
author_role |
author |
author2 |
Ferreira, Tiago Miguel Vicente, Romeu Lourenço, Paulo B. Pena, Fernando |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Eudave, Rafael Ramirez Ferreira, Tiago Miguel Vicente, Romeu Lourenço, Paulo B. Pena, Fernando |
dc.subject.por.fl_str_mv |
Geographical Information System adobe damage database field survey machine learning seismic damages seismic vulnerability assessment Engenharia e Tecnologia::Engenharia Civil Arts & Humanities Science & Technology |
topic |
Geographical Information System adobe damage database field survey machine learning seismic damages seismic vulnerability assessment Engenharia e Tecnologia::Engenharia Civil Arts & Humanities Science & Technology |
description |
In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04 2023-04-01T00:00:00Z |
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 |
https://hdl.handle.net/1822/88552 |
url |
https://hdl.handle.net/1822/88552 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ramírez Eudave, R., Ferreira, T. M., Vicente, R., Lourenco, P. B., & Peña, F. (2023, April 16). Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes. International Journal of Architectural Heritage. Informa UK Limited. http://doi.org/10.1080/15583058.2023.2200739 1558-3058 10.1080/15583058.2023.2200739 https://www.tandfonline.com/doi/full/10.1080/15583058.2023.2200739 |
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 |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
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 |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 |
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1799137422095679488 |