Dynamic risk assessment framework for offshore pipelines corrosion under insulation

Corrosion under insulation (CUI) remains a critical threat to the integrity and reliability of offshore and marine pipeline systems due to its hidden nature, complex degradation mechanisms, and limited inspection strategies. This study presents a dynamic, probabilistic risk assessment framework that integrates a Dynamic Bayesian Network (DBN)-based susceptibility model with an independent three-state degradation formulation to evaluate CUI-induced failure evolution and the associated risks. Pipeline condition is classified into three degradation states: low, moderate, and high, whose time-dependent transitions are modelled using state-dependent exponential degradation functions informed by literature data and expert judgment. System state evolution is represented using matrix exponential solutions, enabling a probabilistic assessment of pipeline condition over time. The framework supports Bayesian updating as new inspection or operational evidence becomes available and explicitly links time-dependent failure probabilities to consequence severity classification and economic loss estimation. A case study is presented to demonstrate the application of the framework in assessing CUI susceptibility, failure progression, and the relative effectiveness of inspection and mitigation strategies. The results indicate that combined inspection and insulation-related interventions are more effective in reducing severe and catastrophic failure outcomes than isolated control measures, with moderate degradation states acting as transitional stages between recovery and deterioration. While acknowledging that experimental validation and long-term field verification were not feasible within the scope of this study, the proposed framework is intended as a comparative decision-support tool for integrity management in CUI-prone offshore environments. The developed methodological structure allows adaptation and calibration as asset-specific data become available, supporting risk-informed maintenance planning, expected loss quantification, and proactive decision-making for offshore pipeline systems.