Reliable and efficient risk assessments are essential to deal effectively with potential risks in international
construction projects. However, most conventional risk modeling methods are based on the hypothesis that risk factors are independent,
which does not account adequately for the causal relationships among risk factors. In this study, a risk assessment model for international
construction projects was developed to improve the efficacy of risk management by integrating fault tree analysis and fuzzy set theory
with a Bayesian belief network. The risk rating of each risk factor, expressed as the product of risk occurrence probability and impact,
was incorporated into the risk assessment model to evaluate degrees of risk. Therefore, risk factors were categorized into different risk
levels taking into account their inherent causal relationships, which allowed the identification of critical risk factors. The applicability
of the fuzzy Bayesian belief network-based risk assessment model was verified using a case study through a comparative analysis with
the results from a fuzzy synthetic evaluation method. The comparison shows that the proposed risk assessment model is able to provide
guidelines for an effective risk management process and ultimately to increase project performance in a complex environment such as
international construction projects.
A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia