Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Latin American journal of solids and structures (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252022000300510 |
Resumo: | Abstract Peak ground acceleration (PGA) is frequently used to describe ground motions accurately to defined the zone is critical for structural engineering design. This study developed a novel models for predicting the PGA using Artificial Neural Networks-Gravitational Search Algorithm (ANN-GSA) and Response Surface Methodology (RSM). This paper grants the prediction of PGA for the seismotectonic of Iraq, which is considered the earlier attempt in Iraqi region. The magnitude of the earthquake, the average shear-wave velocity, the focal depth, the distance between the station, and the earthquake source were used in this study. The proposed models are constructed using a database of 187 previous ground motion records, this dataset is also utilized to evaluate the effect of PGA’s parameters. In general, the results demonstrate that the newly proposed models exhibit a high degree of correlation, perfect mean values, a low coefficient of variance, fewer errors, and an acceptable performance index value compared to actual PGA values. However, the composite ANN-GSA model performs better than the RSM model. |
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Latin American journal of solids and structures (Online) |
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Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization TechniquesPeak ground acceleration (PGA)Artificial neural network (ANN)Response Surface Methodology (RSM)Analyse factorial designGravitational Search Algorithm (GSA)Analysis of variance (ANOVA)Abstract Peak ground acceleration (PGA) is frequently used to describe ground motions accurately to defined the zone is critical for structural engineering design. This study developed a novel models for predicting the PGA using Artificial Neural Networks-Gravitational Search Algorithm (ANN-GSA) and Response Surface Methodology (RSM). This paper grants the prediction of PGA for the seismotectonic of Iraq, which is considered the earlier attempt in Iraqi region. The magnitude of the earthquake, the average shear-wave velocity, the focal depth, the distance between the station, and the earthquake source were used in this study. The proposed models are constructed using a database of 187 previous ground motion records, this dataset is also utilized to evaluate the effect of PGA’s parameters. In general, the results demonstrate that the newly proposed models exhibit a high degree of correlation, perfect mean values, a low coefficient of variance, fewer errors, and an acceptable performance index value compared to actual PGA values. However, the composite ANN-GSA model performs better than the RSM model.Associação Brasileira de Ciências Mecânicas2022-20-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252022000300510Latin American Journal of Solids and Structures v.19 n.3 2022reponame:Latin American journal of solids and structures (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/1679-78256940info:eu-repo/semantics/openAccessHason,Mahir M.Al-Zuhairi,Alaa HusseinHanoon,Ammar N.Abdulhameed,Ali A.Al Zand,Ahmed W.Abbood,Imad S.eng2022-06-02T00:00:00Zoai:scielo:S1679-78252022000300510Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=1679-7825&lng=pt&nrm=isohttps://old.scielo.br/oai/scielo-oai.phpabcm@abcm.org.br||maralves@usp.br1679-78251679-7817opendoar:2022-06-02T00:00Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false |
dc.title.none.fl_str_mv |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
title |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
spellingShingle |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques Hason,Mahir M. Peak ground acceleration (PGA) Artificial neural network (ANN) Response Surface Methodology (RSM) Analyse factorial design Gravitational Search Algorithm (GSA) Analysis of variance (ANOVA) |
title_short |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
title_full |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
title_fullStr |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
title_full_unstemmed |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
title_sort |
Peak Ground Acceleration Models Predictions Utilizing Two Metaheuristic Optimization Techniques |
author |
Hason,Mahir M. |
author_facet |
Hason,Mahir M. Al-Zuhairi,Alaa Hussein Hanoon,Ammar N. Abdulhameed,Ali A. Al Zand,Ahmed W. Abbood,Imad S. |
author_role |
author |
author2 |
Al-Zuhairi,Alaa Hussein Hanoon,Ammar N. Abdulhameed,Ali A. Al Zand,Ahmed W. Abbood,Imad S. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Hason,Mahir M. Al-Zuhairi,Alaa Hussein Hanoon,Ammar N. Abdulhameed,Ali A. Al Zand,Ahmed W. Abbood,Imad S. |
dc.subject.por.fl_str_mv |
Peak ground acceleration (PGA) Artificial neural network (ANN) Response Surface Methodology (RSM) Analyse factorial design Gravitational Search Algorithm (GSA) Analysis of variance (ANOVA) |
topic |
Peak ground acceleration (PGA) Artificial neural network (ANN) Response Surface Methodology (RSM) Analyse factorial design Gravitational Search Algorithm (GSA) Analysis of variance (ANOVA) |
description |
Abstract Peak ground acceleration (PGA) is frequently used to describe ground motions accurately to defined the zone is critical for structural engineering design. This study developed a novel models for predicting the PGA using Artificial Neural Networks-Gravitational Search Algorithm (ANN-GSA) and Response Surface Methodology (RSM). This paper grants the prediction of PGA for the seismotectonic of Iraq, which is considered the earlier attempt in Iraqi region. The magnitude of the earthquake, the average shear-wave velocity, the focal depth, the distance between the station, and the earthquake source were used in this study. The proposed models are constructed using a database of 187 previous ground motion records, this dataset is also utilized to evaluate the effect of PGA’s parameters. In general, the results demonstrate that the newly proposed models exhibit a high degree of correlation, perfect mean values, a low coefficient of variance, fewer errors, and an acceptable performance index value compared to actual PGA values. However, the composite ANN-GSA model performs better than the RSM model. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-20-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252022000300510 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252022000300510 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1679-78256940 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
publisher.none.fl_str_mv |
Associação Brasileira de Ciências Mecânicas |
dc.source.none.fl_str_mv |
Latin American Journal of Solids and Structures v.19 n.3 2022 reponame:Latin American journal of solids and structures (Online) instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
instname_str |
Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
instacron_str |
ABCM |
institution |
ABCM |
reponame_str |
Latin American journal of solids and structures (Online) |
collection |
Latin American journal of solids and structures (Online) |
repository.name.fl_str_mv |
Latin American journal of solids and structures (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) |
repository.mail.fl_str_mv |
abcm@abcm.org.br||maralves@usp.br |
_version_ |
1754302890985390080 |