Design optimization of steel structures considering uncertainties

Papadrakakis, M; Lagaros, ND; Plevris, V

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Plevris, V	en
dc.date.accessioned	2014-03-01T01:22:07Z
dc.date.available	2014-03-01T01:22:07Z
dc.date.issued	2005	en
dc.identifier.issn	0141-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16466
dc.subject	Evolution Strategies	en
dc.subject	Monte Carlo simulation	en
dc.subject	Neural Networks	en
dc.subject	Reliability analysis	en
dc.subject	Robust design	en
dc.subject	Structural optimization	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Computer simulation	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimization	en
dc.subject.other	Performance	en
dc.subject.other	Structural design	en
dc.subject.other	Engineering applications	en
dc.subject.other	Reliability-based design optimization (RBDO)	en
dc.subject.other	Robust design optimization (RDO)	en
dc.subject.other	Structural response	en
dc.subject.other	Steel structures	en
dc.subject.other	structural analysis	en
dc.title	Design optimization of steel structures considering uncertainties	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.engstruct.2005.04.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.engstruct.2005.04.002	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In real world engineering applications the uncertainties of the structural parameters are inherent and the scatter from their nominal ideal values is in most cases unavoidable. These uncertainties play a dominant role in structural performance and the only way to assess this influence is to perform Reliability-Based Design Optimization (RBDO) and Robust Design Optimization (RDO). Compared to the basic deterministic-based optimization problem, a RBDO problem considers additional non-deterministic constraint functions, while the RDO yields a design with a state of robustness, so that its performance is the least sensitive to the variability of the uncertain parameters. The first part of this study examines the application of Neural Networks (NN) to the RBDO of large-scale structural systems, while the second part investigates the structural RDO problem. The use of NN in the framework of the RBDO problem is motivated by the approximate concepts inherent in reliability analysis and the time-consuming repeated analyses required by Monte Carlo Simulation. On the other hand the RDO is a multi-criteria optimization problem where the aim is to minimize both the weight of the structure and the variance of the structural response. (c) 2005 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Engineering Structures	en
dc.identifier.doi	10.1016/j.engstruct.2005.04.002	en
dc.identifier.isi	ISI:000230383300010	en
dc.identifier.volume	27	en
dc.identifier.issue	9	en
dc.identifier.spage	1408	en
dc.identifier.epage	1418	en