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|Title||MODELLING THE PARAMETRIC CONSTRUCTION PROJECT COST ESTIMATE USING ANN|
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.
|Published in||Creative Construction Conference 2012 June 30–July 3, 2012 Budapest, Hungary|
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|El-Sawalhi, Nabil I._26.pdf||21.89Mb|