Early cost estimating of road tunnel construction using neural networks

Petroutsatou, K; Georgopoulos, E; Lambropoulos, S; Pantouvakis, JP

dc.contributor.author	Petroutsatou, K	en
dc.contributor.author	Georgopoulos, E	en
dc.contributor.author	Lambropoulos, S	en
dc.contributor.author	Pantouvakis, JP	en
dc.date.accessioned	2014-03-01T02:08:42Z
dc.date.available	2014-03-01T02:08:42Z
dc.date.issued	2012	en
dc.identifier.issn	07339364	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29711
dc.subject	Construction costs	en
dc.subject	Estimation	en
dc.subject	Neural networks	en
dc.subject	Tunnel construction	en
dc.subject.other	Basic parameters	en
dc.subject.other	Construction costs	en
dc.subject.other	Cost estimate	en
dc.subject.other	Cost estimation system	en
dc.subject.other	Cost estimations	en
dc.subject.other	Design decisions	en
dc.subject.other	General regression neural network	en
dc.subject.other	Multi-layer feed-forward networks	en
dc.subject.other	Northern Greece	en
dc.subject.other	Real world data	en
dc.subject.other	Road tunnel	en
dc.subject.other	Total length	en
dc.subject.other	Tunnel construction	en
dc.subject.other	Cost accounting	en
dc.subject.other	Estimation	en
dc.subject.other	Neural networks	en
dc.subject.other	Roads and streets	en
dc.subject.other	Tunnels	en
dc.subject.other	Cost estimating	en
dc.title	Early cost estimating of road tunnel construction using neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1061/(ASCE)CO.1943-7862.0000479	en
heal.identifier.secondary	http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000479	en
heal.publicationDate	2012	en
heal.abstract	Road tunnel construction is subject to underground uncertainties and risks, and as such it is difficult to predict the final construction cost, especially at the conception phase where issues are evaluated and important design decisions are made. A system assisting in the early cost estimation of road tunnels would therefore be of great value as it would allow the quick costing of alternative and more economical solutions. The development of such an early cost estimation system is discussed in this paper. First, the basic parameters (geological, geometrical, and work quantities-related) affecting temporary and permanent support and final construction cost are determined. After that, appropriate real-world data derived from the analysis of 33 twin tunnels of 46km total length constructed for the Egnatia Motorway in northern Greece from 1998 to 2004 and related to work quantities is collected and normalized. Appropriate price lists are then applied to calculate the costs; subsequently, cost-estimating models are developed using two types of neural networks: (1)the multilayer feed-forward network; and (2)the general regression neural network. Finally, these models are compared against real quantities and costs for accuracy and robustness. The main conclusion is that the models developed are fit for their purpose and may lead to fairly accurate work quantities and cost estimates of road tunnels. © 2012 American Society of Civil Engineers.	en
heal.journalName	Journal of Construction Engineering and Management	en
dc.identifier.doi	10.1061/(ASCE)CO.1943-7862.0000479	en
dc.identifier.volume	138	en
dc.identifier.issue	6	en
dc.identifier.spage	679	en
dc.identifier.epage	687	en