Probabilistic Risk Assessment in Safety-Critical Systems

Probabilistic risk assessment (PRA) is an essential framework for evaluating the likelihood and consequences of unwanted events in safety-critical systems such as nuclear power plants, chemical facilities and aerospace platforms. By systematically identifying failure modes, quantifying uncertainties and modelling dependencies, PRA enables decision-makers to allocate resources effectively and enhance system resilience. Core elements include hazard identification, system modelling through techniques such as fault tree and event tree analysis, and uncertainty quantification via statistical and expert-elicited data. Recent advances integrate dynamic system behaviour, data-driven approaches and digital-twin technologies to capture time-dependent interactions and real-time risk metrics. This evolution has broadened PRA’s applicability, from traditional engineered systems to complex socio-technical infrastructures exposed to emerging threats such as cyber-physical interactions and climate-related hazards. Practical implementation emphasises computational efficiency, validation against historical incidents and the design of robust safety barriers. As regulatory and societal demand for demonstrable safety grows, PRA continues to drive global standards and inform policy in domains ranging from transport networks to critical energy grids.

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Probabilistic Risk Assessment in Safety-Critical Systems publication trend

The graph below shows the total number of articles in probabilistic risk assessment in safety-critical systems across all publications each year (not limited to Nature Index journals).

Technical terms

Probabilistic Risk Assessment (PRA): A systematic framework for quantifying the likelihood and consequences of failures in complex systems based on probability theory and uncertainty analysis.

Fault Tree Analysis (FTA): A deductive modelling technique that represents system failures as logical combinations of component faults using Boolean logic.

Event Tree Analysis (ETA): An inductive method that maps possible outcomes of an initiating event as branches representing sequential success or failure of safety functions.

Bayesian Network (BN): A graphical model that captures probabilistic relationships among variables using directed acyclic graphs and conditional probability tables.

Monte Carlo Simulation: A computational method that employs random sampling to approximate the probability distribution of complex systems under uncertainty.

References

1. Dynamic vulnerability assessment of process plants with respect to vapor cloud explosions. Reliability Engineering & System Safety (2020).
2. A dynamic multi-agent approach for modeling the evolution of multi-hazard accident scenarios in chemical plants. Reliability Engineering & System Safety (2021).