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http://hdl.handle.net/20.500.12358/26508
TitleMODELLING THE PARAMETRIC CONSTRUCTION PROJECT COST ESTIMATE USING ANN
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Abstract

Cost estimation has its proven importance as one of essential factors for project success. The aim of this research is to predict the early project cost estimating using Neural Network. Early project cost estimates represent a key component in business unit decisions. The most important factors effecting the parametric cost estimation in construction building projects in Gaza Strip were defined and investigated. A questionnaire survey and relative index ranking technique were used to conclude the most important factors. 14 most effective factors were identified. 106 Case studies from real executed construction project in Gaza Strip were collected for training and testing the model. The cases were prepared to be used in cost estimate Neural Networks model. 80% of case studies were used to train and test the model. The remaining 20% was used for model verification. The results revealed the ability to the model to predict cost estimate to an acceptable degree of accuracy. The minimum squares error with 0.005 in training stage and 0.021 in testing stage were recorded.

Authors
El-Sawalhi, Nabil I.
TypeJournal Article
Date2012
Published inCreative Construction Conference 2012 June 30–July 3, 2012 Budapest, Hungary
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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