A dynamic barriers implementation in Bayesian-based bow tie diagrams for risk analysis

Bow tie diagrams have become popular methods in risk analysis and safety management. This tool describes graphically, in the same scheme, the whole scenario of an identified risk and its respective preventive and protective barriers. The major problem with bow tie diagrams is that they remain limited by their technical level and by their restriction to a graphical representation of different scenario without any consideration to the dynamic aspect of real systems. Recently we have proposed a new Bayesian approach to construct bow tie diagrams for risk analysis [1]. This approach learns the bow tie structure from real data and improves them by adding a new numerical component allowing us to model in a more realistic manner the system behavior. In this paper we propose to extend this approach by adding the barriers implementation in order to construct the whole bow ties. To this end we will use the numerical component, previously defined in the learning phase, and the analytic hierarchical process (AHP).