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http://hdl.handle.net/20.500.12358/26451
TitleForecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model
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Abstract

Purpose – This paper seeks to introduce an evolved hybrid genetic algorithm and neural network (GNN) model. The model is developed to predict contractor performance given the current attributes in a process to pre-qualify the most appropriate contractor. The predicted performance is used to pre-qualify the contractors. Design/methodology/approach – Hypothetical and real-life case studies from projects executed in the Gaza Strip and West Bank were collected through structured questionnaires. The evaluation of the contractor's attributes and the corresponding actual performance of the contractor in terms of time, cost, and quality overrun (OR) were collected. The weighted contractor's attributes were used as inputs to the GNN model. The corresponding time, cost, and quality ORs for the same cases were fed as outputs to the GNN model in a supervised learning back propagation neural network (NN). (The …

Authors
El-Sawalhi, Nabil I.
Eaton, David
Rustom, Rifat N.
TypeJournal Article
Date2008
Published inConstruction Innovation
SeriesVolume: 8, Number: 4
PublisherEmerald Group Publishing Limited
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  • Staff Publications- Faculty of Engineering [908]
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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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