Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures

TL;DR

This paper introduces a Bayesian decision analysis framework to quantify the benefits of strain-based SHM for monitoring corrosion in ship hulls, aiming to improve maintenance decision-making.

Contribution

It presents a novel VoI framework for ship hull SHM, integrating decision-maker risk perception and comparing monitoring strategies with traditional inspections.

Findings

Strain-based SHM can significantly reduce maintenance costs.

The framework quantifies the value of information for different monitoring strategies.

Risk perception influences optimal inspection timing.

Abstract

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.