Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions
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/1822/88937 |
Resumo: | Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs. |
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Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motionsartificial neural network (ANN)buckling restrained brace frame (BRBF)feature selectionglobal drift ratio (GDR)maximum inter-storey drift ratio (MIDR)pulse-wise real ground motion recordsEngenharia e Tecnologia::Engenharia CivilBuckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs.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, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020.MDPIUniversidade do MinhoMohammadi, AmirhosseinKarimzadeh, ShaghayeghYaghmaei-Sabegh, SamanRanjbari, MaryamLourenço, Paulo B.20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88937engMohammadi, A.; Karimzadeh, S.; Yaghmaei-Sabegh, S.; Ranjbari, M.; Lourenço, P.B. Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions. Buildings 2023, 13, 2542. https://doi.org/10.3390/buildings1310254210.3390/buildings13102542https://www.mdpi.com/2075-5309/13/10/2542info: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:RCAAP2024-02-24T01:26:51Zoai:repositorium.sdum.uminho.pt:1822/88937Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:13.333550Repositório 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 |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
title |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
spellingShingle |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions Mohammadi, Amirhossein artificial neural network (ANN) buckling restrained brace frame (BRBF) feature selection global drift ratio (GDR) maximum inter-storey drift ratio (MIDR) pulse-wise real ground motion records Engenharia e Tecnologia::Engenharia Civil |
title_short |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
title_full |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
title_fullStr |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
title_full_unstemmed |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
title_sort |
Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions |
author |
Mohammadi, Amirhossein |
author_facet |
Mohammadi, Amirhossein Karimzadeh, Shaghayegh Yaghmaei-Sabegh, Saman Ranjbari, Maryam Lourenço, Paulo B. |
author_role |
author |
author2 |
Karimzadeh, Shaghayegh Yaghmaei-Sabegh, Saman Ranjbari, Maryam Lourenço, Paulo B. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Mohammadi, Amirhossein Karimzadeh, Shaghayegh Yaghmaei-Sabegh, Saman Ranjbari, Maryam Lourenço, Paulo B. |
dc.subject.por.fl_str_mv |
artificial neural network (ANN) buckling restrained brace frame (BRBF) feature selection global drift ratio (GDR) maximum inter-storey drift ratio (MIDR) pulse-wise real ground motion records Engenharia e Tecnologia::Engenharia Civil |
topic |
artificial neural network (ANN) buckling restrained brace frame (BRBF) feature selection global drift ratio (GDR) maximum inter-storey drift ratio (MIDR) pulse-wise real ground motion records Engenharia e Tecnologia::Engenharia Civil |
description |
Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
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/1822/88937 |
url |
https://hdl.handle.net/1822/88937 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mohammadi, A.; Karimzadeh, S.; Yaghmaei-Sabegh, S.; Ranjbari, M.; Lourenço, P.B. Utilising Artificial Neural Networks for Assessing Seismic Demands of Buckling Restrained Braces Due to Pulse-like Motions. Buildings 2023, 13, 2542. https://doi.org/10.3390/buildings13102542 10.3390/buildings13102542 https://www.mdpi.com/2075-5309/13/10/2542 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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