The potential of region-specific machine-learning-based ground motion models: application to Turkey
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/1822/88942 |
Resumo: | Conventional ground motion models have extensively been established worldwide based on classical regression analysis of records. Alternatively, advanced nonparametric machine-learning (ML) algorithms may capture the complex nonlinear behaviour of earthquake motions. This paper investigates the efficiency of artificial neural network (ANN) and extreme gradient boosting (XGBoost) in predicting peak ground acceleration (PGA), peak ground velocity (PGV) and pseudo-spectral acceleration (PSA) (period, T = 0.03–2.0 s) for the Turkish dataset. The dataset involves 1166 records of 383 events with a moment magnitude (Mw) of 4.0–7.6, Joyner and Boore distance (RJB) of 0–200 km, focal depth (FD) less than 35 km, and site condition as the averaged shear wave velocity of the soil on the top 30 m (VS30) of 131–1380 m/s. The performance of the models is compared against empirical models in terms of root-mean-square error (RMSE), coefficient of determination (R2), Pearson correlation coefficient (r), and inter-event and intra-event residuals. To perform residual analysis, a likelihood function is developed. Findings reveal that the XGBoost approach gives an unbiased model with a higher correlation and lower residual than ANN. Finally, an online platform is provided for any interested users. |
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The potential of region-specific machine-learning-based ground motion models: application to TurkeyArtificial neural networkExtreme gradient boostingGround motion modelInter-event and intra-event residualsLikelihood functionTurkish ground motion datasetEngenharia e Tecnologia::Engenharia CivilConventional ground motion models have extensively been established worldwide based on classical regression analysis of records. Alternatively, advanced nonparametric machine-learning (ML) algorithms may capture the complex nonlinear behaviour of earthquake motions. This paper investigates the efficiency of artificial neural network (ANN) and extreme gradient boosting (XGBoost) in predicting peak ground acceleration (PGA), peak ground velocity (PGV) and pseudo-spectral acceleration (PSA) (period, T = 0.03–2.0 s) for the Turkish dataset. The dataset involves 1166 records of 383 events with a moment magnitude (Mw) of 4.0–7.6, Joyner and Boore distance (RJB) of 0–200 km, focal depth (FD) less than 35 km, and site condition as the averaged shear wave velocity of the soil on the top 30 m (VS30) of 131–1380 m/s. The performance of the models is compared against empirical models in terms of root-mean-square error (RMSE), coefficient of determination (R2), Pearson correlation coefficient (r), and inter-event and intra-event residuals. To perform residual analysis, a likelihood function is developed. Findings reveal that the XGBoost approach gives an unbiased model with a higher correlation and lower residual than ANN. Finally, an online platform is provided for any interested users.This work has received funding from multiple sources. The national funds from FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UIDB/04029/2020, and the Associate Laboratory Advanced Production and Intelligent Systems ARISE, reference LA/P/0112/2020, provided partial financial support for this study. Additionally, the research was partly funded by the STAND4HERITAGE project, which received financial support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program, Grant agreement No. 833123, as an Advanced Grant. The first author also acknowledges the support of national funds through FCT, under grant agreement 2020.08876.BD.Elsevier Science BVUniversidade do MinhoMohammadi, AmirhosseinKarimzadeh, ShaghayeghBanimahd, Seyed AmirOzsarac, VolkanLourenço, Paulo B.20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88942engMohammadi, A., Karimzadeh, S., Banimahd, S. A., Ozsarac, V., & Lourenço, P. B. (2023, September). The potential of region-specific machine-learning-based ground motion models: Application to Turkey. Soil Dynamics and Earthquake Engineering. Elsevier BV. http://doi.org/10.1016/j.soildyn.2023.1080080267-726110.1016/j.soildyn.2023.108008https://www.sciencedirect.com/science/article/pii/S0267726123002531info: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:RCAAP2024-02-24T01:26:28Zoai:repositorium.sdum.uminho.pt:1822/88942Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:11.641695Repositório 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 potential of region-specific machine-learning-based ground motion models: application to Turkey |
title |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
spellingShingle |
The potential of region-specific machine-learning-based ground motion models: application to Turkey Mohammadi, Amirhossein Artificial neural network Extreme gradient boosting Ground motion model Inter-event and intra-event residuals Likelihood function Turkish ground motion dataset Engenharia e Tecnologia::Engenharia Civil |
title_short |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
title_full |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
title_fullStr |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
title_full_unstemmed |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
title_sort |
The potential of region-specific machine-learning-based ground motion models: application to Turkey |
author |
Mohammadi, Amirhossein |
author_facet |
Mohammadi, Amirhossein Karimzadeh, Shaghayegh Banimahd, Seyed Amir Ozsarac, Volkan Lourenço, Paulo B. |
author_role |
author |
author2 |
Karimzadeh, Shaghayegh Banimahd, Seyed Amir Ozsarac, Volkan 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 Banimahd, Seyed Amir Ozsarac, Volkan Lourenço, Paulo B. |
dc.subject.por.fl_str_mv |
Artificial neural network Extreme gradient boosting Ground motion model Inter-event and intra-event residuals Likelihood function Turkish ground motion dataset Engenharia e Tecnologia::Engenharia Civil |
topic |
Artificial neural network Extreme gradient boosting Ground motion model Inter-event and intra-event residuals Likelihood function Turkish ground motion dataset Engenharia e Tecnologia::Engenharia Civil |
description |
Conventional ground motion models have extensively been established worldwide based on classical regression analysis of records. Alternatively, advanced nonparametric machine-learning (ML) algorithms may capture the complex nonlinear behaviour of earthquake motions. This paper investigates the efficiency of artificial neural network (ANN) and extreme gradient boosting (XGBoost) in predicting peak ground acceleration (PGA), peak ground velocity (PGV) and pseudo-spectral acceleration (PSA) (period, T = 0.03–2.0 s) for the Turkish dataset. The dataset involves 1166 records of 383 events with a moment magnitude (Mw) of 4.0–7.6, Joyner and Boore distance (RJB) of 0–200 km, focal depth (FD) less than 35 km, and site condition as the averaged shear wave velocity of the soil on the top 30 m (VS30) of 131–1380 m/s. The performance of the models is compared against empirical models in terms of root-mean-square error (RMSE), coefficient of determination (R2), Pearson correlation coefficient (r), and inter-event and intra-event residuals. To perform residual analysis, a likelihood function is developed. Findings reveal that the XGBoost approach gives an unbiased model with a higher correlation and lower residual than ANN. Finally, an online platform is provided for any interested users. |
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/1822/88942 |
url |
https://hdl.handle.net/1822/88942 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Mohammadi, A., Karimzadeh, S., Banimahd, S. A., Ozsarac, V., & Lourenço, P. B. (2023, September). The potential of region-specific machine-learning-based ground motion models: Application to Turkey. Soil Dynamics and Earthquake Engineering. Elsevier BV. http://doi.org/10.1016/j.soildyn.2023.108008 0267-7261 10.1016/j.soildyn.2023.108008 https://www.sciencedirect.com/science/article/pii/S0267726123002531 |
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 |
Elsevier Science BV |
publisher.none.fl_str_mv |
Elsevier Science BV |
dc.source.none.fl_str_mv |
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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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