Risk induced contingency cost modeling for power plant projects

Abstract The current practice of expert judgment-based contingency cost allocation by owners lacks a holistic understanding of project risks, their causal relationships, and impact on project costs. This study presents an integrated fuzzy set theory and fuzzy Bayesian belief network model for a rational, realistic determination of contingency costs for infrastructure projects. The application of the model is demonstrated for real-world power plant projects in Bangladesh. The model has promising results for its ability to establish the amount of contingency costs with a maximum error of 20% between the contingency cost predicted with the model and the actual contingency cost. It has the potential to assist both the owner and contractor to set aside a realistic amount of contingency cost in the preliminary phase of a project. The approach is also equally useful for monitoring and controlling project risks, and dynamically updates the contingency cost amount during project execution.