A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures

Finite element model updating has become a key tool to improve the numerical modelling of existing civil engineering structures, by adjusting the numerical response to the observed experimental behaviour of the structure. At present, model updating is mostly conducted using the maximum likelihood me...

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Autores: Naranjo Pérez, Javier, Infantes, María, Jiménez Alonso, Javier Fernando, Sáez Pérez, Andrés
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/180728
Acceso en línea:https://hdl.handle.net/11441/180728
https://doi.org/10.1016/j.engstruct.2020.111327
Access Level:acceso abierto
Palabra clave:Multi-objective harmony search optimization
Machine learning
Collaborative algorithm
Best Pareto solution
Finite element model updating
Maximum likelihood method
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spelling A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structuresNaranjo Pérez, JavierInfantes, MaríaJiménez Alonso, Javier FernandoSáez Pérez, AndrésMulti-objective harmony search optimizationMachine learningCollaborative algorithmBest Pareto solutionFinite element model updatingMaximum likelihood methodFinite element model updating has become a key tool to improve the numerical modelling of existing civil engineering structures, by adjusting the numerical response to the observed experimental behaviour of the structure. At present, model updating is mostly conducted using the maximum likelihood method. Following this approach, the updating problem can be transformed into a multi-objective optimization problem. Due to the complex nonlinear behaviour of the resulting objective functions, metaheuristic optimization algorithms are normally employed to solve such optimization problem. However, and although this is nowadays a well-established technique, there are still two main drawbacks that need to be addressed for practical engineering applications, namely: (i) the high simulation time required to compute the problem; and (ii) the uncertainty associated with the selection of the best updated model among all the Pareto optimal solutions. In order to overcome these limitations, a new collaborative algorithm is proposed herein, which takes advantage of the collaborative coupling among two optimization algorithms (harmony search and active-set algorithms), a machine learning technique (artificial neural networks) and a statistical tool (principal component analysis). The implementation details of our proposal are discussed in detail throughout the paper and its performance is illustrated with a case study addressing the model updating of a real steel footbridge. Two are the main advantages of the newly proposed algorithm: (i) it leads to a clear reduction of the simulation time; and (ii) it further permits a robust selection of the best updated model.ElsevierMecánica de Medios Continuos y Teoría de EstructurasEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Ministerio de Economía y Competitividad (MINECO). España2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/180728https://doi.org/10.1016/j.engstruct.2020.111327reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEngineering Structures, 225, 111327.RTI2018-094945-B-C21https://www.sciencedirect.com/science/article/pii/S0141029620339286info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1807282026-06-17T12:51:07Z
dc.title.none.fl_str_mv A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
title A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
spellingShingle A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
Naranjo Pérez, Javier
Multi-objective harmony search optimization
Machine learning
Collaborative algorithm
Best Pareto solution
Finite element model updating
Maximum likelihood method
title_short A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
title_full A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
title_fullStr A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
title_full_unstemmed A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
title_sort A collaborative machine learning-optimization algorithm to improve the finite element model updating of civil engineering structures
dc.creator.none.fl_str_mv Naranjo Pérez, Javier
Infantes, María
Jiménez Alonso, Javier Fernando
Sáez Pérez, Andrés
author Naranjo Pérez, Javier
author_facet Naranjo Pérez, Javier
Infantes, María
Jiménez Alonso, Javier Fernando
Sáez Pérez, Andrés
author_role author
author2 Infantes, María
Jiménez Alonso, Javier Fernando
Sáez Pérez, Andrés
author2_role author
author
author
dc.contributor.none.fl_str_mv Mecánica de Medios Continuos y Teoría de Estructuras
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Multi-objective harmony search optimization
Machine learning
Collaborative algorithm
Best Pareto solution
Finite element model updating
Maximum likelihood method
topic Multi-objective harmony search optimization
Machine learning
Collaborative algorithm
Best Pareto solution
Finite element model updating
Maximum likelihood method
description Finite element model updating has become a key tool to improve the numerical modelling of existing civil engineering structures, by adjusting the numerical response to the observed experimental behaviour of the structure. At present, model updating is mostly conducted using the maximum likelihood method. Following this approach, the updating problem can be transformed into a multi-objective optimization problem. Due to the complex nonlinear behaviour of the resulting objective functions, metaheuristic optimization algorithms are normally employed to solve such optimization problem. However, and although this is nowadays a well-established technique, there are still two main drawbacks that need to be addressed for practical engineering applications, namely: (i) the high simulation time required to compute the problem; and (ii) the uncertainty associated with the selection of the best updated model among all the Pareto optimal solutions. In order to overcome these limitations, a new collaborative algorithm is proposed herein, which takes advantage of the collaborative coupling among two optimization algorithms (harmony search and active-set algorithms), a machine learning technique (artificial neural networks) and a statistical tool (principal component analysis). The implementation details of our proposal are discussed in detail throughout the paper and its performance is illustrated with a case study addressing the model updating of a real steel footbridge. Two are the main advantages of the newly proposed algorithm: (i) it leads to a clear reduction of the simulation time; and (ii) it further permits a robust selection of the best updated model.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/180728
https://doi.org/10.1016/j.engstruct.2020.111327
url https://hdl.handle.net/11441/180728
https://doi.org/10.1016/j.engstruct.2020.111327
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Engineering Structures, 225, 111327.
RTI2018-094945-B-C21
https://www.sciencedirect.com/science/article/pii/S0141029620339286
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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