Computational intelligence tools for the prediction of slope performance

Ferentinou, MD; Sakellariou, MG

dc.contributor.author	Ferentinou, MD	en
dc.contributor.author	Sakellariou, MG	en
dc.date.accessioned	2014-03-01T01:26:02Z
dc.date.available	2014-03-01T01:26:02Z
dc.date.issued	2007	en
dc.identifier.issn	0266-352X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17892
dc.subject	Artificial neural networks	en
dc.subject	Back propagation	en
dc.subject	Earthquake induced displacements	en
dc.subject	Kohonen self-organizing maps	en
dc.subject	Slope stability	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Geological	en
dc.subject.classification	Geosciences, Multidisciplinary	en
dc.subject.other	Acceleration	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Backpropagation algorithms	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Geographic information systems	en
dc.subject.other	Self organizing maps	en
dc.subject.other	Earthquake induced displacement	en
dc.subject.other	Soil classification	en
dc.subject.other	Landslides	en
dc.subject.other	artificial neural network	en
dc.subject.other	back propagation	en
dc.subject.other	Bayesian analysis	en
dc.subject.other	dynamic analysis	en
dc.subject.other	estimation method	en
dc.subject.other	failure mechanism	en
dc.subject.other	GIS	en
dc.subject.other	hazard assessment	en
dc.subject.other	prediction	en
dc.subject.other	self organization	en
dc.subject.other	slope failure	en
dc.subject.other	slope stability	en
dc.title	Computational intelligence tools for the prediction of slope performance	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compgeo.2007.06.004	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compgeo.2007.06.004	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The current paper illustrates the application of computational intelligence tools in slope performance prediction both in static and dynamic conditions. We present the results obtained by using the back-propagation algorithm, the theory of Bayesian neural networks and the Kohonen self-organizing maps, one of the most realistic models of the biological brain functions. We estimate slope stability controlling variables by combining computational intelligence tools with generic interaction matrix theory. Our emphasis is given to the prediction and estimation of the following: slope stability, coefficient of critical acceleration, earthquake induced displacements, unsaturated soil classification, classification according to the status of stability and failure mechanism for dry and wet slopes. Finally, we present an integrated methodology for assessing landslide hazard coupling computational intelligence tools and geographical information systems. (c) 2007 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Computers and Geotechnics	en
dc.identifier.doi	10.1016/j.compgeo.2007.06.004	en
dc.identifier.isi	ISI:000250947600004	en
dc.identifier.volume	34	en
dc.identifier.issue	5	en
dc.identifier.spage	362	en
dc.identifier.epage	384	en