Soft computing techniques in parameter identification and probabilistic seismic analysis of structures

Tsompanakis, Y; Lagaros, ND; Stavroulakis, GE

dc.contributor.author	Tsompanakis, Y	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Stavroulakis, GE	en
dc.date.accessioned	2014-03-01T01:29:12Z
dc.date.available	2014-03-01T01:29:12Z
dc.date.issued	2008	en
dc.identifier.issn	09659978	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19156
dc.subject	Artificial neural networks	en
dc.subject	Inverse problems	en
dc.subject	Probabilistic seismic analysis	en
dc.subject	Simulation	en
dc.subject	Structural identification	en
dc.subject.other	Algorithms	en
dc.subject.other	Inverse problems	en
dc.subject.other	Neural networks	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Soft computing	en
dc.subject.other	Statistical tests	en
dc.subject.other	Back-propagation algorithms	en
dc.subject.other	Probabilistic seismic analysis	en
dc.subject.other	Structural identification	en
dc.subject.other	Seismic design	en
dc.title	Soft computing techniques in parameter identification and probabilistic seismic analysis of structures	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.advengsoft.2007.06.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.advengsoft.2007.06.004	en
heal.publicationDate	2008	en
heal.abstract	The objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive engineering problems. Two types of applications have been considered, namely parameter (flaw) identification and probabilistic seismic analysis of structures. Artificial neural networks (ANNs) based metamodels are used in order to replace the time-consuming repeated structural analyses. The back-propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data. The numerical tests demonstrate the computational advantages of the proposed methodologies. © 2007 Elsevier Ltd. All rights reserved.	en
heal.journalName	Advances in Engineering Software	en
dc.identifier.doi	10.1016/j.advengsoft.2007.06.004	en
dc.identifier.volume	39	en
dc.identifier.issue	7	en
dc.identifier.spage	612	en
dc.identifier.epage	624	en