Modelling TBM performance with artificial neural networks

Benardos, AG; Kaliampakos, DC

dc.contributor.author	Benardos, AG	en
dc.contributor.author	Kaliampakos, DC	en
dc.date.accessioned	2014-03-01T01:21:05Z
dc.date.available	2014-03-01T01:21:05Z
dc.date.issued	2004	en
dc.identifier.issn	0886-7798	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16057
dc.subject	Advance rate modelling	en
dc.subject	Artificial neural networks	en
dc.subject	TBM tunnelling	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Geology	en
dc.subject.other	Geotechnical engineering	en
dc.subject.other	Neural networks	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Project management	en
dc.subject.other	Geotechnical sites	en
dc.subject.other	Tunneling process	en
dc.subject.other	Tunneling (excavation)	en
dc.subject.other	advance rate	en
dc.subject.other	artificial neural network	en
dc.subject.other	performance assessment	en
dc.subject.other	TBM	en
dc.subject.other	tunneling	en
dc.title	Modelling TBM performance with artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.tust.2004.02.128	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.tust.2004.02.128	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	Assessing TBM performance is an important parameter for the successful accomplishment of a tunnelling project. This paper presents an attempt to model the advance rate of tunnelling with respect to the geological and geotechnical site conditions. The model developed for this particular task is implemented through the use of an artificial neural network (ANN) that allows the identification and understanding of both the way and the extent that the involved parameters affect the tunnelling process. The model described in the paper is customised for the construction of an interstation section of the Athens metro tunnels, where the ANN generalisations provided precise estimations regarding the anticipated advance rate. (C) 2004 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Tunnelling and Underground Space Technology	en
dc.identifier.doi	10.1016/j.tust.2004.02.128	en
dc.identifier.isi	ISI:000223815000006	en
dc.identifier.volume	19	en
dc.identifier.issue	6	en
dc.identifier.spage	597	en
dc.identifier.epage	605	en