Awad, A L S (2012) Intelligent contractor default prediction model for surety bonding in the construction industry. PhD thesis, University of Alberta, Canada.
Abstract
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.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Fayek, A R |
| Uncontrolled Keywords: | failure; genetic algorithms; optimization; surety; uncertainty; construction project; artificial neural network; expert system; prequalification; owner; professional; stakeholder; Canada; fuzzy logic; neural network |
| Index terms: | methodology, construction project, practitioner, expert system, default, genetic algorithm, surety, neural network, artificial neural network, prediction model, propagation, knowledge acquisition, construction industry, optimization technique, judgment, fuzzy logic, owner, aggregate, adaptation, Canada |
| Subjects: | user focus, practitioner, materials science, research methods, algorithms, Geography, production management, warranties, modelling and simulation, prediction and forecasting, data science, artificial intelligence, engineering process, dispute resolution, sociology, data management, student development, industry analysis |
| Topics: | Research Practice, Roles and Professions, Stakeholder Management, Digital Applications, Design Practice, Contract Administration, Engineering Principles, Project Management, Geographical Context, Legal Issues, Education |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here