Applying bayesian networks to safety causation analysis and modeling in socio-technical systems: bridging theory and practice

Safety causation analysis is of paramount importance in socio-technical systems, playing a crucial role in identifying and mitigating potential hazards and risks to ensure safe and resilient system operations. Understanding the causative factors behind safety incidents and system malfunctions is also crucial for implementing effective preventive measures and enhancing system resilience. In this context, the use of Bayesian networks (BNs) becomes imperative, as they offer indispensable tools for representing uncertain and complex relationships among system elements under conditions of uncertainty. This chapter explores the applications of BNs in safety causation analysis within socio-technical systems and demonstrated through a comprehensive examination of a scenario involving Slop leakage from a process tank. The applicability of knowledge-driven and data-driven approaches as a complement to BNs is also highlighted. It is followed by demonstrations of how BN features such as dependency analysis, predictive and diagnostic capabilities, sensitivity analysis, and dynamic modeling can improve risk assessment and decision-making processes. Through a detailed case study of Slop leakage from a process tank, the practical implications of BNs are illustrated, providing valuable insights for safety professionals and system managers. This chapter serves as a comprehensive guide, bridging scientific principles with practical applications to ensure system safety within socio-technical systems.