Construction Innovation: Information, Process, Management

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

Document Information:

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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Contractors Prequalification: Methods and Techniques

Product Description

This Book explains the existing practices of contractors pre-qualification in newly developed countries. The most known models for contractor's pre-qualification that could be used for contractor's pre-qualification is presented. The Book concentrates on client's pre-qualification for contractors at the level of standing list. The main focus of this Book is the integration of contractor pre-qualification attributes with the project success factors (client¿s objectives) in terms of time, cost and quality (i.e. How the client could predict the contractors performance given his current attributes).

 For this purpose, a Genetic Neural Network (GNN) model has built an automated system to predict the output of a given contractor attributes in terms of overruns (OR) of contractor's performance factors (time, cost and quality). This book is written for both clients and contractors. The project managers who are involved in contracts procurement, tendering and contractors selection are targeted. The contractors should be of interest of this book to cope with the requirement of clients conditions for contractor selection

About the Author

Dr. Nabil I El-Sawalhi received his PhD degree from the School of the Built Environment, The University of Salford, UK. He is currently occupying the Emergency Shelter Assistance Coordinator at UNRWA-Gaza Strip. The author is the chairman of Engineering Arbitration Center - Engineers Syndicate-Gaza Governorate.

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Things you probably didn't know about the Kindle

As an outsider I had no idea that the kindle has a wide open linux root. You can install not only a port of FBreader to read epubs and other files, but there is also a port of mplayer to listen to music including internet radio via wifi, font patches, ssh, different screen savers and other things. I just wanted to test it for two weeks as you can in Europe with pretty much any mail ordered thing. But now I'll keep it!

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