Methodology for project risk assessment using Bayesian belief networks in engineering construction projects

Odimabo, O. O.
Oduoza, Chike F.
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DEStech Publications, Inc.

Engineering construction projects commonly suffer from cost and time overruns, for most of the time because of uncertainties that are not carefully considered during bidding for contracts and budget project planning. These uncertainties place the project at risk of poor quality delivery and also not adhering to the time and budget schedule within the original contractual agreement. A clear focus on risk analysis and its management from the onset is essential to guide project planning and also to achieve optimal performance in construction projects. The research carried out here presents a risk assessment methodology based on the Bayesian belief network, which is an effective tool for knowledge representation and reasoning under conditions of uncertainty, structural learning procedure, combination of different source of knowledge, explicit treatment of uncertainty and support for decision analysis and fast responses for risk assessment. Bayesian belief network therefore, is a scenario planning tool suitable for project risk management because of its systematic and integrated process approach to the analysis of key risk factors affecting project delivery, with a view to predict the worst and best case scenarios and thereby guide project planning. The proposed methodology developed in this study is partly based on knowledge and experiences acquired from experts who are in a position to provide information on the sources of uncertainty, and the causes of uncertain condition with a view to generate optimal response strategies to support a successful project outcome.

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio
Includes bibliographical references
Construction projects--Management, Risk assessment, Project management, Bayesian statistical decision theory