Bayesian value-of-information optimization for cost–risk–reliability-based ranking of offshore platforms

Structural Integrity Management (SIM) of offshore fixed platforms plays a critical role in ensuring operational safety and controlling life-cycle maintenance costs. This paper presents an integrated Bayesian Value-of-Information (VoI) framework for prioritizing inspection and repair actions across offshore platforms. The novelty of this work lies in developing, for the first time, a VoI-based decision-support framework that combines SACS-based Reserve Stress Ratio (RSR) analysis with structural reliability and pre-posterior Bayesian updating to rank multiple offshore platforms according to the economic value of new structural health monitoring (SHM) and inspection information. This innovation enables a more realistic and quantitative link between structural analysis, probabilistic deterioration modeling, and cost-effective SIM planning. In the proposed method, the prior risk of each platform (derived from available design and condition data) is updated using an expected likelihood function formulated through pre-posterior analysis. The framework is applied to ten offshore platforms with diverse functions, design years, and structural configurations. The results indicate that platforms P8, P4, P6, P2, P1, P9, and P5 offer the highest expected value for inspection and repair actions, whereas platforms P3, P7, and P10 demonstrate low VoI and are not prioritized. Overall, the proposed Bayesian VoI framework provides a quantitatively practical decision-support tool for structural health monitoring–informed inspection and repair planning of offshore platforms.