Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures
For ship operators and maintenance engineers, this provides a first-of-its-kind quantitative framework to justify SHM adoption in hull maintenance.
This study quantifies the value of information (VoI) from strain-based SHM for monitoring corrosion-induced thickness loss in ship hulls using Bayesian pre-posterior decision analysis, showing that SHM can reduce expected costs by up to 30% compared to traditional inspections.
Recent advances in Structural Health Monitoring (SHM) have attracted industry interest, yet real-world applications, such as in ship structures remain scarce. Despite SHM's potential to optimise maintenance, its adoption in ships is limited due to the lack of clearly quantifiable benefits for hull maintenance. This study employs a Bayesian pre-posterior decision analysis to quantify the value of information (VoI) from SHM systems monitoring corrosion-induced thickness loss (CITL) in ship hulls, in a first-of-its-kind analysis for ship structures. We define decision-making consequence cost functions based on exceedance probabilities relative to a target CITL threshold, which can be set by the decision-maker. This introduces a practical aspect to our framework, that enables implicitly modelling the decision-maker's risk perception. We apply this framework to a large-scale, high-fidelity numerical model of a commercial vessel and examine the relative benefits of different CITL monitoring strategies, including strain-based SHM and traditional on-site inspections.