Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation

Papadrakakis, M; Papadopoulos, V; Lagaros, ND

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Papadopoulos, V	en
dc.contributor.author	Lagaros, ND	en
dc.date.accessioned	2014-03-01T01:12:19Z
dc.date.available	2014-03-01T01:12:19Z
dc.date.issued	1996	en
dc.identifier.issn	0045-7825	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12059
dc.subject	Back Propagation Algorithm	en
dc.subject	Complex Structure	en
dc.subject	Critical Loads	en
dc.subject	Difference Set	en
dc.subject	Importance Sampling	en
dc.subject	Monte Carlo Simulation	en
dc.subject	Probability of Failure	en
dc.subject	Random Variable	en
dc.subject	Reliability Analysis	en
dc.subject	Structural Reliability	en
dc.subject	Neural Network	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.classification	Mathematics, Interdisciplinary Applications	en
dc.subject.classification	Mechanics	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computer aided analysis	en
dc.subject.other	Elasticity	en
dc.subject.other	Failure (mechanical)	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Plasticity	en
dc.subject.other	Random processes	en
dc.subject.other	Reliability	en
dc.subject.other	Sampling	en
dc.subject.other	Critical load factor	en
dc.subject.other	Elastic plastic structures	en
dc.subject.other	Failure probability	en
dc.subject.other	Importance sampling	en
dc.subject.other	Plastic collapse	en
dc.subject.other	Structural analysis	en
dc.title	Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0045-7825(96)01011-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0045-7825(96)01011-0	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	This paper examines the application of Neural Networks (NN) to the reliability analysis of complex structural systems in connection with Monte Carlo Simulation (MCS). The failure of the system is associated with the plastic collapse. The use of NN was motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required for MCS. A Back Propagation algorithm is implemented for training the NN utilising available information generated from selected elasto-plastic analyses. The trained NN is then used to compute the critical load factor due to different sets of basic random variables leading to close prediction of the probability of failure. The use of MCS with Importance Sampling further improves the prediction of the probability of failure with Neural Networks.	en
heal.publisher	ELSEVIER SCIENCE SA LAUSANNE	en
heal.journalName	Computer Methods in Applied Mechanics and Engineering	en
dc.identifier.doi	10.1016/0045-7825(96)01011-0	en
dc.identifier.isi	ISI:A1996VH09200008	en
dc.identifier.volume	136	en
dc.identifier.issue	1-2	en
dc.identifier.spage	145	en
dc.identifier.epage	163	en