Construction Innovation: Information, Process, Management

Forecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model

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Title:	Forecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model
Author(s):	Nabil El-Sawalhi, (Butlu (Research Institute for the Built and Human Environment), School of the Built Environment, University of Salford, Salford, UK), David Eaton, (Butlu (Research Institute for the Built and Human Environment), School of the Built Environment, University of Salford, Salford, UK), Rifat Rustom, (Civil Engineering Department, Faculty of Engineering, The Islamic University-Gaza, Gaza, Palestine)
Citation:	Nabil El-Sawalhi, David Eaton, Rifat Rustom, (2008) "Forecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model", Construction Innovation: Information, Process, Management, Vol. 8 Iss: 4, pp.280 - 298
Keywords:	Neural nets, Performance management, Supplier evaluation
Article type:	Research paper
DOI:	10.1108/14714170810912662 (Permanent URL)
Publisher:	Emerald Group Publishing Limited
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 adopted training and testing process to develop a trained model is presented.) The training process, including choosing the topology of the required NN using genetic algorithms, is explained. Findings – The results revealed that there is a satisfactory relationship between the contractor attributes and the corresponding performance in terms of contractor's deviation from the client objectives. The accuracy of the model in terms of mean absolute percentage error (MAPE), R2, average absolute error and mean square error revealed that the model has sufficient accuracy for implementation. The average MAPE for time, cost and quality OR is 15 per cent. Consequently, the model accuracy is 85 per cent. Originality/value – The GNN model is able to predict future contractor performance for given attributes.

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