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Intelligent Contractor Default Prediction Model for Surety Bonding in the Construction Industry Open Access


Other title
fuzzy expert systems
fuzzy logic
Surety Bonding
Contractor Default
Type of item
Degree grantor
University of Alberta
Author or creator
Awad, Adel Ls
Supervisor and department
Aminah Robinson Fayek (Department of Civil and Environmental Engineering)
Examining committee member and department
Dr. Petr Musilek (Department of Electrical and Computer Engineering, University of Alberta)
Dr. Ming Lu (Department of Civil and Environmental Engineering, University of Alberta)
Dr. Lloyd M. Waugh (Department of Civil Engineering, University of New Brunswick)
Dr. John Doucette (Department of Mechanical Engineering, University of Alberta)
Dr. Aminah Robinson Fayek (Department of Civil and Environmental Engineering, University of Alberta)
Department of Civil and Environmental Engineering
Construction Engineering and Management
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Construction is a risk-filled, uncertain, and dynamic environment. Contractor default is a critical risk that can influence the outcome of projects in the construction industry. Construction project owners and other stakeholders look for methods to predict the potential of contractors to default, in order to avoid awarding contracts to high-risk contractors. One of the most effective tools for project owners to mitigate the risk of contractor failure is to transfer the risk of project completion to a surety company. The surety company conducts a comprehensive prequalification (underwriting) process to assess the possibility of contractor default. The prequalification process is done to evaluate any contractor, project, and contractual risks that may affect the contractor’s performance. The prequalification process involves evaluating various qualitative and quantitative evaluation criteria, many of which contain uncertainty and require subjective judgment. This thesis demonstrates how fuzzy logic and expert systems techniques are integrated to develop a model able to help surety professionals in contractor default prediction for a specific construction project for bonding purposes. Building the contractor default prediction model (CDPM) included identifying, classifying, and providing a comprehensive, detailed list of the evaluation criteria for contractor and project prequalification. Numerical scales were defined for the quantitative evaluation criteria, and rating scales, using reference variables, were developed to quantify the qualitative criteria. An important evaluation category, “contractor’s organizational practices,” was incorporated as input to the CDPM. The CDPM was built using the expertise of surety practitioners across Canada, and several different knowledge acquisition techniques were used. A novel methodology for finding a group consensus function that aggregates experts’ judgment scores to represent a common opinion was applied, in order to aggregate the experts’ inputs for the CDPM development. A methodology to apply two different optimization techniques, genetic algorithms and artificial neural network back-propagation, for the CDPM’s adaptation is presented. Finally, software for contractor default prediction, SuretyQualification, is developed.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Awad, A., and Fayek, A. Robinson. (2012b). “A decision support system for contractor prequalification for surety bonding.” Journal of Automation in Construction, 21, 89–98.Awad, A., and Fayek, A. R. (2010). “Developing a framework for construction contractor qualification for surety bonding.” Proceedings, ASCE Construction Research Congress, Banff, AB, May 8–10, Vol. 2: 899–908.Awad, A., and Fayek, A. Robinson. (2012). “Contractor default prediction model for surety bonding.” Canadian Journal of Civil Engineering, in press.Awad, A., and Fayek, A. Robinson. (2012b). “Adaptive learning of contractor default prediction model for surety bonding.” Journal of Construction Engineering and Management, 30 manuscript pages, submitted February 17, 2012.Awad, A., and Fayek, A. Robinson. (2011). “Adaptive learning for fuzzy expert systems for construction applications.” CSCE Annual General Conference, Ottawa, ON, June 14–17.

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