Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement

7 páginas, 7 figuras, 3 tablas.-- El Pdf es la versión post-print de autor.

Detalles Bibliográficos
Autores: Perera, Ricardo, Arteaga Iriarte, Ángel, Diego, Ana de
Tipo de recurso: artículo
Fecha de publicación:2010
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/40704
Acceso en línea:http://hdl.handle.net/10261/40704
Access Level:acceso abierto
Palabra clave:Shear strengthening
FRP
Reinforced concrete
Neural networks
Genetic algorithms
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spelling Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcementPerera, RicardoArteaga Iriarte, ÁngelDiego, Ana deShear strengtheningFRPReinforced concreteNeural networksGenetic algorithms7 páginas, 7 figuras, 3 tablas.-- El Pdf es la versión post-print de autor.The prediction of the shear capacity of reinforced concrete beams retrofitted in shear by means of externally bonded FRP is very complex as demonstrate the studies carried out up to date. As alternative to the conventional methods two approaches based on artificial intelligence are proposed for the first time. Firstly, the use of neural networks as a means of predicting shear capacity without the need of using complex models and, secondly, the use of genetic algorithms as a means of determining suitably how the shear mechanism works. Predictions obtained with both approaches are compared to experimental values.The writers acknowledge support for the work reported in this paper from the Spanish Ministry of Education and Science (project BIA2007-67790).Peer reviewedElsevier201120112010info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/40704reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.compstruct.2009.10.027info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/407042026-05-22T06:33:51Z
dc.title.none.fl_str_mv Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
title Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
spellingShingle Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
Perera, Ricardo
Shear strengthening
FRP
Reinforced concrete
Neural networks
Genetic algorithms
title_short Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
title_full Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
title_fullStr Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
title_full_unstemmed Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
title_sort Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
dc.creator.none.fl_str_mv Perera, Ricardo
Arteaga Iriarte, Ángel
Diego, Ana de
author Perera, Ricardo
author_facet Perera, Ricardo
Arteaga Iriarte, Ángel
Diego, Ana de
author_role author
author2 Arteaga Iriarte, Ángel
Diego, Ana de
author2_role author
author
dc.subject.none.fl_str_mv Shear strengthening
FRP
Reinforced concrete
Neural networks
Genetic algorithms
topic Shear strengthening
FRP
Reinforced concrete
Neural networks
Genetic algorithms
description 7 páginas, 7 figuras, 3 tablas.-- El Pdf es la versión post-print de autor.
publishDate 2010
dc.date.none.fl_str_mv 2010
2011
2011
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/40704
url http://hdl.handle.net/10261/40704
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.compstruct.2009.10.027
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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