A novel approach to managing uncertainty in risk assessment using integrated z-numbers and intuitionistic fuzzy sets:...

Accurate risk assessment in industrial systems is frequently challenged by uncertainty in expert judgments and system behavior. This study proposes a novel approach that integrates Intuitionistic Z-Numbers (IZNs) with the System Hazard Identification, Prediction, and Prevention (SHIPP) methodology to improve predictive reliability. IZNs capture both expert estimations and the associated confidence levels when determining the prior probabilities of basic events (BEs), thereby reducing uncertainty more effectively than conventional intuitionistic fuzzy sets. These enhanced estimates are incorporated into the SHIPP framework-utilizing fault and event tree analyses-and updated with real-world incident data via Bayesian inference. A case study involving liquefied petroleum gas (LPG) spherical storage tanks illustrates the practical application of the proposed method. Results indicate that the use of IZNs enhances the reliability of posterior probability estimates for barrier failures and associated consequences, ultimately supporting more informed and resilient risk management decisions in high-hazard industries.