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.

翻译：结构健康监测（SHM）领域的最新进展已引起工业界的关注，然而在实际应用（如船舶结构）中仍然较为有限。尽管SHM在优化维护方面具有潜力，但由于缺乏对船体维护效益的明确量化，其在船舶领域的应用受到制约。本研究首次针对船舶结构，采用贝叶斯先验后验决策分析方法，量化了监测船体腐蚀引起厚度损失（CITL）的SHM系统所提供的信息价值（VoI）。我们基于相对于目标CITL阈值（可由决策者设定）的超限概率，定义了决策后果成本函数。这为我们的框架引入了实用性，能够隐式地建模决策者的风险感知。我们将该框架应用于一艘商船的大规模高保真数值模型，并比较了不同CITL监测策略（包括基于应变的SHM和传统的现场检测）的相对效益。