Simulating the seismic response of embankments via artificial neural networks

Tsompanakis, Y; Lagaros, ND; Psarropoulos, PN; Georgopoulos, EC

dc.contributor.author	Tsompanakis, Y	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Psarropoulos, PN	en
dc.contributor.author	Georgopoulos, EC	en
dc.date.accessioned	2014-03-01T01:31:54Z
dc.date.available	2014-03-01T01:31:54Z
dc.date.issued	2009	en
dc.identifier.issn	0965-9978	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19971
dc.subject	Artificial neural networks	en
dc.subject	Embankments	en
dc.subject	Material nonlinearity	en
dc.subject	Seismic response	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Accurate predictions	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Design process	en
dc.subject.other	Earthquake engineerings	en
dc.subject.other	Equivalent-linear	en
dc.subject.other	Finite-element methods	en
dc.subject.other	Geo-materials	en
dc.subject.other	Geostructures	en
dc.subject.other	Geotechnical	en
dc.subject.other	Material nonlinearity	en
dc.subject.other	Non-linear behaviors	en
dc.subject.other	SC techniques	en
dc.subject.other	Science and technologies	en
dc.subject.other	Time-consuming process	en
dc.subject.other	Backpropagation	en
dc.subject.other	Canals	en
dc.subject.other	Civil engineering	en
dc.subject.other	Dynamic response	en
dc.subject.other	Earthquakes	en
dc.subject.other	Embankments	en
dc.subject.other	Engineering geology	en
dc.subject.other	Geotechnical engineering	en
dc.subject.other	Hydraulic structures	en
dc.subject.other	Seismic response	en
dc.subject.other	Soft computing	en
dc.subject.other	Neural networks	en
dc.title	Simulating the seismic response of embankments via artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.advengsoft.2008.11.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.advengsoft.2008.11.005	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Geotechnical earthquake engineering may generally be considered as an ""imprecise"" scientific area due to the unavoidable uncertainties and the simplifications adopted during the design process of geostructures. Therefore, relatively accurate predictions using advanced soft computing (SC) techniques can be tolerated rather than solving a problem conventionally. Artificial neural networks (ANNs), being one of the most popular SC techniques, have been used in many fields of science and technology, as well as, into an increasing number of earthquake engineering applications on structures and infrastructures. In this work the implementation of ANNs is focused on the simulation of the seismic response of a typical embankment. The dynamic response of the embankment is evaluated utilizing the finite-element method, where the nonlinear behavior of the geo-materials can be taken into account by an equivalent-linear procedure. In the present study, this extremely time-consuming process is replaced by properly trained ANNs. © 2008 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Advances in Engineering Software	en
dc.identifier.doi	10.1016/j.advengsoft.2008.11.005	en
dc.identifier.isi	ISI:000266339000012	en
dc.identifier.volume	40	en
dc.identifier.issue	8	en
dc.identifier.spage	640	en
dc.identifier.epage	651	en