Fuzzy Bayesian network analysis for quantifying risk reduction rate of Hierarchy of Controls

Abstract

In industries dealing with chemical substances, accidents can pose threats not only the workplace but also to neighboring communities. Therefore, it is crucial to assess and manage these risks. In South Korea, conducting risk assessments is mandatory as a preventive measure to avert accidents. However, determining the acceptability of risk levels and estimating the effectiveness of risk-reducing measures can be challenging during these assessments, despite prioritizing existing measures. This study focuses on evaluating the risk reduction rate of the Hierarchy of Controls. To address the challenges associated with estimating the risk reduction rate, especially in the face of unpredictability and uncertainties, we utilized the Fuzzy Bayesian Network (FBN). FBN combines Fuzzy set theory with the Bayesian Network, providing a more reliable approach to risk assessment. Specifically, our study examines quantifying the risk reduction rate of Controls concerning fire and explosion risks, considering the severity of potential accidents. The findings from this research have the potential to enhance the efficiency of decision-making processes in risk assessments, contributing to improved safety measures.