Risk Engineering

Risk engineering applies quantitative and qualitative methods to identify, analyse and mitigate hazards across technical and socio-technical systems. Core approaches include probabilistic risk assessment, which uses fault and event tree models to quantify the likelihood and consequences of component failures; reliability engineering, which models component degradation and remaining useful life; and resilience engineering, which designs systems to absorb and recover from unforeseen disturbances. Recent advances exploit machine learning and data-driven frameworks—such as Bayesian networks, graph-based neural models and digital twins—to capture complex dependencies, dynamic interactions and evolving operational states. Robust optimisation and stochastic programming techniques account for uncertain parameters in design and planning, yielding solutions that perform satisfactorily across a range of scenarios. Complementary methods, from fuzzy logic and belief-divergence measures in expert elicitation to multi-objective trade-off analysis, support decision-making where data are sparse or stakeholder values diverge. Together these tools enable practitioners to forecast hazards, prioritise interventions and continuously adapt to new information, thereby reducing exposure to catastrophic failures and improving system resilience.

Research from Nature Portfolio

A unified limit-state equation has been developed for steel pipelines subjected concurrently to internal pressure, axial force and bending moment. By deriving a three-dimensional stress model and evaluating multiple yield criteria against experimental failure data, this work provides a theoretical basis for predicting burst pressure under complex service conditions and guides integrity assessments in high-demand environments.

To address uncertainty in expert judgements, an improved method based on a belief-divergence measure has been proposed for Failure Mode and Effects Analysis. By quantifying the divergence among expert assessments and converting relative support into expert weights, this approach yields more reliable risk priority numbers and enhances the consistency of FMEA outcomes in industrial applications.

Research from all publishers

Geometric deep learning models have been applied to real-time prediction of cascading failures in power grids. Trained on synthetic outage scenarios, graph neural networks capture topological and operational features to deliver early warnings of cascade propagation, enabling targeted countermeasures and reducing blackout risk.

A deep learning framework has been demonstrated for pitting corrosion modelling in buried transmission pipelines. By integrating soil characteristics and coating parameters as inputs to a multilayer neural network, the model achieves superior accuracy in forecasting maximum pit depths compared with empirical correlations, supporting proactive maintenance planning.

A hybrid genetic-neural approach has been introduced for hazardous materials routing. A neural network screens road segments by risk attributes, and a multi-objective robust optimisation model employs genetic algorithms to generate Pareto-optimal routes that balance transit time, cost and accident probability under uncertain traffic conditions.

Risk Engineering publication trend

The graph below shows the total number of articles in risk engineering across all publications each year (not limited to Nature Index journals).

Technical terms

Limit-state equation: A mathematical relation defining the stress or pressure at which a structure fails under combined loading.

Belief-divergence measure: A metric for quantifying inconsistency among expert probability assessments, used to derive expert weights in risk analysis.

Bayesian network: A probabilistic graphical model representing dependencies among risk factors via directed acyclic graphs.

Geometric deep learning: A class of machine-learning methods that process data structured as graphs to capture relational patterns for prediction tasks.

Robust optimisation: An approach that seeks solutions resilient to parameter uncertainty by ensuring acceptable performance across defined scenarios.

Cascading failure: A sequence of dependent outages in which one component’s failure increases stress on connected elements, leading to further failures.

References

1. Limit state equation and failure pressure prediction model of pipeline with complex loading. Nature Communications (2024).
2. Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure. Scientific Reports (2022).
3. Geometric deep learning for online prediction of cascading failures in power grids. Reliability Engineering & System Safety (2023).
4. Predictive deep learning for pitting corrosion modeling in buried transmission pipelines. Process Safety and Environmental Protection (2023).
5. Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm. PLOS ONE (2018).