Artificial intelligence in underground development: A study of TBM performance

Benardos, A

dc.contributor.author	Benardos, A	en
dc.date.accessioned	2014-03-01T02:51:33Z
dc.date.available	2014-03-01T02:51:33Z
dc.date.issued	2008	en
dc.identifier.issn	17433509	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35556
dc.subject	Artificial neural networks	en
dc.subject	TBM performance modelling	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Backpropagation	en
dc.subject.other	Boring machines (machine tools)	en
dc.subject.other	Earth boring machines	en
dc.subject.other	Network performance	en
dc.subject.other	Tunneling machines	en
dc.subject.other	Tunnels	en
dc.subject.other	Artificial intelligence techniques	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Consistent performance	en
dc.subject.other	Feed-forward artificial neural networks	en
dc.subject.other	Geotechnical	en
dc.subject.other	TBM performance modelling	en
dc.subject.other	Tunnel boring machines	en
dc.subject.other	Tunnel operations	en
dc.subject.other	Neural networks	en
dc.subject.other	artificial intelligence	en
dc.subject.other	artificial neural network	en
dc.subject.other	performance assessment	en
dc.subject.other	TBM	en
dc.subject.other	underground construction	en
dc.title	Artificial intelligence in underground development: A study of TBM performance	en
heal.type	conferenceItem	en
heal.identifier.primary	10.2495/US080031	en
heal.identifier.secondary	http://dx.doi.org/10.2495/US080031	en
heal.publicationDate	2008	en
heal.abstract	Modelling tunnel boring machine (TBM) performance is an important aspect in tunnel operations. The use of artificial intelligence techniques such as artificial neural networks has been recently introduced to this subject and the results from such applications prove their potential in making accurate prognosis. This paper presents a review of feed-forward artificial neural network (ANN) development and furthermore it illustrates their application by the use of two cases studies from Italian and Greek underground projects, where the TBM performance is modelled. The results obtained show that the developed ANNs can efficiently generalise the TBM behaviour in their respective geotechnical environment, having a reliable, effective and consistent performance.	en
heal.journalName	WIT Transactions on the Built Environment	en
dc.identifier.doi	10.2495/US080031	en
dc.identifier.volume	102	en
dc.identifier.spage	21	en
dc.identifier.epage	32	en