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Article: Structural reliability analysis for implicit performance functions using artificial neural network

TitleStructural reliability analysis for implicit performance functions using artificial neural network
Authors
KeywordsArtificial neural network
Finite element method
First-order reliability method
Implicit performance function
Monte-Carlo simulation
Second-order reliability method
Issue Date2005
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/strusafe
Citation
Structural Safety, 2005, v. 27 n. 1, p. 25-48 How to Cite?
AbstractThe Monte-Carlo simulation (MCS), the first-order reliability methods (FORM) and the second-order reliability methods (SORM), are three reliability analysis methods that are commonly used for structural safety evaluation. The MCS requires the calculations of hundreds and thousands of performance function values. The FORM and SORM demand the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. Such implicit performance functions are normally encountered when the structural systems are complicated and numerical analysis such as finite element methods has to be adopted for the prediction. To address this issue, this paper presents three artificial neural network (ANN)-based reliability analysis methods, i.e. ANN-based MCS, ANN-based FORM, and ANN-based SORM. These methods employ multi-layer feedforward ANN technique to approximate the implicit performance functions. The ANN technique uses a small set of the actual values of the implicit performance functions. Such a small set of actual data is obtained via normal numerical analysis such as finite element methods for the complicated structural system. They are used to develop a trained ANN generalization algorithm. Then a large number of the values and partial derivatives of the implicit performance functions can be obtained for conventional reliability analysis using MCS, FORM or SORM. Examples are given in the paper to illustrate why and how the proposed ANN-based structural reliability analysis can be carried out. The results have shown the proposed approach is applicable to structural reliability analysis involving implicit performance functions. The present results are compared well with those obtained by the conventional reliability methods such as the direct Monte-Carlo simulation, the response surface method and the FORM method 2. © 2004 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/70949
ISSN
2015 Impact Factor: 2.086
2015 SCImago Journal Rankings: 1.609
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDeng, Jen_HK
dc.contributor.authorGu, Den_HK
dc.contributor.authorLi, Xen_HK
dc.contributor.authorYue, ZQen_HK
dc.date.accessioned2010-09-06T06:27:33Z-
dc.date.available2010-09-06T06:27:33Z-
dc.date.issued2005en_HK
dc.identifier.citationStructural Safety, 2005, v. 27 n. 1, p. 25-48en_HK
dc.identifier.issn0167-4730en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70949-
dc.description.abstractThe Monte-Carlo simulation (MCS), the first-order reliability methods (FORM) and the second-order reliability methods (SORM), are three reliability analysis methods that are commonly used for structural safety evaluation. The MCS requires the calculations of hundreds and thousands of performance function values. The FORM and SORM demand the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. Such implicit performance functions are normally encountered when the structural systems are complicated and numerical analysis such as finite element methods has to be adopted for the prediction. To address this issue, this paper presents three artificial neural network (ANN)-based reliability analysis methods, i.e. ANN-based MCS, ANN-based FORM, and ANN-based SORM. These methods employ multi-layer feedforward ANN technique to approximate the implicit performance functions. The ANN technique uses a small set of the actual values of the implicit performance functions. Such a small set of actual data is obtained via normal numerical analysis such as finite element methods for the complicated structural system. They are used to develop a trained ANN generalization algorithm. Then a large number of the values and partial derivatives of the implicit performance functions can be obtained for conventional reliability analysis using MCS, FORM or SORM. Examples are given in the paper to illustrate why and how the proposed ANN-based structural reliability analysis can be carried out. The results have shown the proposed approach is applicable to structural reliability analysis involving implicit performance functions. The present results are compared well with those obtained by the conventional reliability methods such as the direct Monte-Carlo simulation, the response surface method and the FORM method 2. © 2004 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/strusafeen_HK
dc.relation.ispartofStructural Safetyen_HK
dc.rightsStructural Safety. Copyright © Elsevier BV.en_HK
dc.subjectArtificial neural networken_HK
dc.subjectFinite element methoden_HK
dc.subjectFirst-order reliability methoden_HK
dc.subjectImplicit performance functionen_HK
dc.subjectMonte-Carlo simulationen_HK
dc.subjectSecond-order reliability methoden_HK
dc.titleStructural reliability analysis for implicit performance functions using artificial neural networken_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-4730&volume=27&spage=25&epage=48&date=2004&atitle=Structural+reliability+analysis+for+implicit+performance+functions+using+artificial+neural+networken_HK
dc.identifier.emailYue, ZQ:yueqzq@hkucc.hku.hken_HK
dc.identifier.authorityYue, ZQ=rp00209en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.strusafe.2004.03.004en_HK
dc.identifier.scopuseid_2-s2.0-12144266233en_HK
dc.identifier.hkuros96779en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-12144266233&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume27en_HK
dc.identifier.issue1en_HK
dc.identifier.spage25en_HK
dc.identifier.epage48en_HK
dc.identifier.isiWOS:000225425600002-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridDeng, J=7402612836en_HK
dc.identifier.scopusauthoridGu, D=7202152089en_HK
dc.identifier.scopusauthoridLi, X=8083718800en_HK
dc.identifier.scopusauthoridYue, ZQ=7102782735en_HK

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