Early cost estimating of road tunnel construction using neural networks

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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 http://hdl.handle.net/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

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