Soft computing techniques in probabilistic seismic analysis of structures

dc.creator	Lagaros, N. D.	en
dc.creator	Tsompanakis, Y.	en
dc.creator	Fragiadakis, M.	en
dc.creator	Papadrakakis, M.	en
dc.date.accessioned	2015-11-23T10:37:24Z
dc.date.available	2015-11-23T10:37:24Z
dc.date.issued	2006
dc.identifier	10.4018/978-1-59904-099-8.ch011
dc.identifier.isbn	9781599040998
dc.identifier.uri	http://hdl.handle.net/11615/30155
dc.description.abstract	Earthquake-resistant design of structures using probabilistic analysis is an emerging field in structural engineering. The objective of this chapter is to investigate the efficiency of soft computing methods when incorporated into the solution of computationally intensive earthquake engineering problems. Two methodologies are proposed in this work where limit-state probabilities of exceedance for real world structures are determined. Neural networks based metamodels are used in order to replace a large number of time-consuming structural analyses required for the calculation of a limit-state probability. The Rprop algorithm is employed for the training of the neural networks; using data obtained from appropriately selected structural analyses. © 2007, Idea Group Inc.	en
dc.source	Intelligent Computational Paradigms in Earthquake Engineering	en
dc.source.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84900670378&partnerID=40&md5=5ee670558f0bb75b856c261bb2a99f8a
dc.title	Soft computing techniques in probabilistic seismic analysis of structures	en
dc.type	bookChapter	en

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