Reliability-based structural optimization using neural networks and Monte Carlo simulation

Papadrakakis, M; Lagaros, ND

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Lagaros, ND	en
dc.date.accessioned	2014-03-01T01:18:17Z
dc.date.available	2014-03-01T01:18:17Z
dc.date.issued	2002	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14922
dc.subject	Evolution strategies	en
dc.subject	Monte Carlo simulation	en
dc.subject	Neural networks	en
dc.subject	Parallel computations	en
dc.subject	Reliability analysis	en
dc.subject	Structural optimization	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Approximation theory	en
dc.subject.other	Computer simulation	en
dc.subject.other	Constraint theory	en
dc.subject.other	Elastoplasticity	en
dc.subject.other	Large scale systems	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Plastics	en
dc.subject.other	Probability	en
dc.subject.other	Reliability	en
dc.subject.other	Large-scale structural systems	en
dc.subject.other	Structural optimization	en
dc.subject.other	Monte Carlo simulation	en
dc.subject.other	neural network	en
dc.subject.other	structural analysis	en
dc.title	Reliability-based structural optimization using neural networks and Monte Carlo simulation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0045-7825(02)00287-6	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0045-7825(02)00287-6	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	This paper examines the application of neural networks (NN) to reliability-based structural optimization of large-scale structural systems. The failure of the structural system is associated with the plastic collapse, The optimization part is performed with evolution strategies, while the reliability analysis is carried out with the Monte Carlo simulation (MCS) method incorporating the importance sampling technique for the reduction of the sample size. In this study two methodologies are examined. In the first one an NN is trained to perform both the deterministic and probabilistic constraints check. In the second one only the clasto-plastic analysis phase, required by the MCS, is replaced by a neural network prediction of the structural behaviour up to collapse. The use of NN is motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required by MCS. (C) 2002 Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/S0045-7825(02)00287-6	en
dc.identifier.isi	ISI:000176628700004	en
dc.identifier.volume	191	en
dc.identifier.issue	32	en
dc.identifier.spage	3491	en
dc.identifier.epage	3507	en