A study of slope stability prediction using neural networks

Sakellariou, MG; Ferentinou, MD

dc.contributor.author	Sakellariou, MG	en
dc.contributor.author	Ferentinou, MD	en
dc.date.accessioned	2014-03-01T01:21:46Z
dc.date.available	2014-03-01T01:21:46Z
dc.date.issued	2005	en
dc.identifier.issn	09603182	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16363
dc.subject	Artificial neural networks	en
dc.subject	Back-propagation	en
dc.subject	Factor of safety	en
dc.subject	Geotechnical parameters	en
dc.subject	Slope stability	en
dc.subject	Threshold logic units	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computational methods	en
dc.subject.other	Geotechnical engineering	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Safety factor	en
dc.subject.other	Threshold logic	en
dc.subject.other	Geotechnical parameters	en
dc.subject.other	Slope stability prediction	en
dc.subject.other	Slope stability	en
dc.subject.other	artificial neural network	en
dc.subject.other	slope stability	en
dc.title	A study of slope stability prediction using neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s10706-004-8680-5	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s10706-004-8680-5	en
heal.publicationDate	2005	en
heal.abstract	The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory. © Springer 2005.	en
heal.journalName	Geotechnical and Geological Engineering	en
dc.identifier.doi	10.1007/s10706-004-8680-5	en
dc.identifier.volume	23	en
dc.identifier.issue	4	en
dc.identifier.spage	419	en
dc.identifier.epage	445	en