Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks

Autores
Papadrakakis, M.; Papadopoulus, V.; Lagaros, N.D.; Oliver, J.; Huespe, Alfredo Edmundo; Sánchez, Pablo Javier
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedure is proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach. The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consuming repeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available information generated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure to compute the peak load of the structure due to different sets of basic random variables leading to close prediction of the probability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragility curves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties (modeled as random variables) considered, for both test examples, are the Young’s modulus, the Poisson’s ratio, the tensile strength and the specific fracture energy of the concrete.
Fil: Papadrakakis, M.. Athens University; Grecia
Fil: Papadopoulus, V.. Athens University; Grecia
Fil: Lagaros, N.D.. Athens University; Grecia
Fil: Oliver, J.. Universidad Politecnica de Catalunya; España
Fil: Huespe, Alfredo Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Sánchez, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Materia
Reliability Analysis
Fragility Curves;
Vulnerability
Monte Carlo Simulation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/25398
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural NetworksPapadrakakis, M.Papadopoulus, V.Lagaros, N.D.Oliver, J.Huespe, Alfredo EdmundoSánchez, Pablo JavierReliability AnalysisFragility Curves;VulnerabilityMonte Carlo SimulationProbabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedure is proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach. The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consuming repeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available information generated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure to compute the peak load of the structure due to different sets of basic random variables leading to close prediction of the probability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragility curves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties (modeled as random variables) considered, for both test examples, are the Young’s modulus, the Poisson’s ratio, the tensile strength and the specific fracture energy of the concrete.Fil: Papadrakakis, M.. Athens University; GreciaFil: Papadopoulus, V.. Athens University; GreciaFil: Lagaros, N.D.. Athens University; GreciaFil: Oliver, J.. Universidad Politecnica de Catalunya; EspañaFil: Huespe, Alfredo Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Sánchez, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaElsevier Science2008-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/25398Papadrakakis, M.; Papadopoulus, V.; Lagaros, N.D.; Oliver, J.; Huespe, Alfredo Edmundo; et al.; Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks; Elsevier Science; Structural Safety; 30; 3; 12-2008; 217-2350167-4730CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.strusafe.2006.11.005info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167473006000713info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:09Zoai:ri.conicet.gov.ar:11336/25398instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:10.167CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
title Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
spellingShingle Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
Papadrakakis, M.
Reliability Analysis
Fragility Curves;
Vulnerability
Monte Carlo Simulation
title_short Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
title_full Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
title_fullStr Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
title_full_unstemmed Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
title_sort Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks
dc.creator.none.fl_str_mv Papadrakakis, M.
Papadopoulus, V.
Lagaros, N.D.
Oliver, J.
Huespe, Alfredo Edmundo
Sánchez, Pablo Javier
author Papadrakakis, M.
author_facet Papadrakakis, M.
Papadopoulus, V.
Lagaros, N.D.
Oliver, J.
Huespe, Alfredo Edmundo
Sánchez, Pablo Javier
author_role author
author2 Papadopoulus, V.
Lagaros, N.D.
Oliver, J.
Huespe, Alfredo Edmundo
Sánchez, Pablo Javier
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Reliability Analysis
Fragility Curves;
Vulnerability
Monte Carlo Simulation
topic Reliability Analysis
Fragility Curves;
Vulnerability
Monte Carlo Simulation
dc.description.none.fl_txt_mv Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedure is proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach. The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consuming repeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available information generated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure to compute the peak load of the structure due to different sets of basic random variables leading to close prediction of the probability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragility curves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties (modeled as random variables) considered, for both test examples, are the Young’s modulus, the Poisson’s ratio, the tensile strength and the specific fracture energy of the concrete.
Fil: Papadrakakis, M.. Athens University; Grecia
Fil: Papadopoulus, V.. Athens University; Grecia
Fil: Lagaros, N.D.. Athens University; Grecia
Fil: Oliver, J.. Universidad Politecnica de Catalunya; España
Fil: Huespe, Alfredo Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Sánchez, Pablo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
description Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedure is proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach. The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consuming repeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available information generated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure to compute the peak load of the structure due to different sets of basic random variables leading to close prediction of the probability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragility curves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties (modeled as random variables) considered, for both test examples, are the Young’s modulus, the Poisson’s ratio, the tensile strength and the specific fracture energy of the concrete.
publishDate 2008
dc.date.none.fl_str_mv 2008-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/25398
Papadrakakis, M.; Papadopoulus, V.; Lagaros, N.D.; Oliver, J.; Huespe, Alfredo Edmundo; et al.; Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks; Elsevier Science; Structural Safety; 30; 3; 12-2008; 217-235
0167-4730
CONICET Digital
CONICET
url http://hdl.handle.net/11336/25398
identifier_str_mv Papadrakakis, M.; Papadopoulus, V.; Lagaros, N.D.; Oliver, J.; Huespe, Alfredo Edmundo; et al.; Vulnerability Analysis of Large Concrete Dams using the Continuum Strong Discontinuity Approach and Neural Networks; Elsevier Science; Structural Safety; 30; 3; 12-2008; 217-235
0167-4730
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.strusafe.2006.11.005
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167473006000713
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613016793907200
score 13.070432

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