Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/88942
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Mohammadi, Amirhossein | por |
dc.contributor.author | Karimzadeh, Shaghayegh | por |
dc.contributor.author | Banimahd, Seyed Amir | por |
dc.contributor.author | Ozsarac, Volkan | por |
dc.contributor.author | Lourenço, Paulo B. | por |
dc.date.accessioned | 2024-02-21T18:51:24Z | - |
dc.date.available | 2024-02-21T18:51:24Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | 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 | por |
dc.identifier.issn | 0267-7261 | por |
dc.identifier.uri | https://hdl.handle.net/1822/88942 | - |
dc.description.abstract | 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. | por |
dc.description.sponsorship | 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. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier Science BV | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04029%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/833123/EU | por |
dc.relation | info:eu-repo/grantAgreement/FCT/POR_NORTE/2020.08876.BD/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Artificial neural network | por |
dc.subject | Extreme gradient boosting | por |
dc.subject | Ground motion model | por |
dc.subject | Inter-event and intra-event residuals | por |
dc.subject | Likelihood function | por |
dc.subject | Turkish ground motion dataset | por |
dc.title | The potential of region-specific machine-learning-based ground motion models: application to Turkey | por |
dc.type | article | - |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0267726123002531 | por |
oaire.citationVolume | 172 | por |
dc.date.updated | 2024-02-04T12:34:25Z | - |
dc.identifier.doi | 10.1016/j.soildyn.2023.108008 | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Civil | por |
sdum.export.identifier | 13081 | - |
sdum.journal | Soil Dynamics and Earthquake Engineering | por |
Aparece nas coleções: | ISISE - Artigos em Revistas Internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
2023_SDEE_172_The potential of region-specific machine-learning-based ground motion models Application to Turkey.pdf | 16,69 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons