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arxiv: 2505.07427 · v2 · submitted 2025-05-12 · 📊 stat.AP · cs.CE

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

Nicholas E. Silionis , Konstantinos N. Anyfantis This is my paper

Pith reviewed 2026-05-22 16:28 UTC · model grok-4.3

classification 📊 stat.AP cs.CE
keywords value of informationstructural health monitoringship hull structurescorrosion-induced thickness lossBayesian decision analysisstrain-based monitoringmaintenance optimization
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The pith

Bayesian value-of-information analysis quantifies benefits of strain-based monitoring for corrosion thickness loss in ship hulls.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper applies a Bayesian pre-posterior decision analysis to calculate the value of information from monitoring strategies for corrosion-induced thickness loss in ship hulls. It uses a high-fidelity numerical model of a commercial vessel and defines cost functions based on the probability of exceeding a chosen thickness-loss threshold, allowing the decision-maker's risk attitude to be incorporated implicitly. The analysis compares strain-based structural health monitoring against traditional on-site inspections. A sympathetic reader would care because the approach supplies a concrete numerical basis for deciding whether advanced sensors are worth installing when their payoff has previously been difficult to demonstrate.

Core claim

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,

What carries the argument

Bayesian pre-posterior value-of-information analysis that ranks monitoring strategies by the expected reduction in consequence costs, where costs are computed from the probability of exceeding a user-chosen thickness-loss threshold.

If this is right

  • Different CITL monitoring strategies can be ranked by expected cost savings for any chosen thickness-loss threshold.
  • Strain-based SHM can be shown to deliver higher or lower value than periodic on-site inspections depending on the threshold and risk attitude.
  • Maintenance planning decisions can be updated in advance of data collection by computing the pre-posterior expected value of each strategy.
  • The same cost-function construction can be reused for other degradation modes once the exceedance probability model is available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pre-posterior framework could be applied to fatigue crack monitoring or coating breakdown in the same vessel model.
  • Real-time sensor data streams could be fed back to update the VoI estimates during a voyage rather than only before deployment.
  • The approach offers a template for similar decisions on other large steel structures such as offshore platforms or bridges.
  • Extending the model to include sensor failure rates or data-transmission losses would tighten the link to operational conditions.

Load-bearing premise

The numerical model of the commercial vessel and the chosen consequence cost functions based on exceedance probabilities accurately represent real-world decision consequences and sensor performance under operational conditions.

What would settle it

Compare the predicted cost reductions from the VoI calculations against actual maintenance cost and downtime data collected after installing strain-based sensors on a real operating commercial vessel.

Figures

Figures reproduced from arXiv: 2505.07427 by Konstantinos N. Anyfantis, Nicholas E. Silionis.

Figure 1
Figure 1. Figure 1: Conceptual overview of the proposed VoI-based assessment framework for strain-based monitoring in ship [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Directed graphical models (DGMs) of Bayesian model updating problems considered for thickness loss [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Consequence cost functions for d = {d0, d1}. events, rather than structural failure in the classical sense (yield, buckling, ultimate strength). The consequence costs quantify the economic impact of these maintenance decisions. Accordingly, consequence cost functions are defined for each decision and parametrised with respect to the cumulative exceedance probability. A linear formulation is employed in thi… view at source ↗
Figure 4
Figure 4. Figure 4: Flowchart illustrating the computational workflow of the proposed VoI-based assessment framework for [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: These were obtained by drawing nprior = 1000 independent samples from each distribution in Eq. (21) and propagating them through the deterioration model. An eight-year period, beginning at t0 = 10 years, was assumed during which corrosion is active and simultaneously monitored. The parameter priors were selected through iterative refinement to ensure, together with the model structure, that the resulting t… view at source ↗
Figure 5
Figure 5. Figure 5: Thickness loss prior process realisations expressed as a fraction of the as-built plate thickness [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Perspective view of three hold compartment FE model, (b) regions with different thickness levels, (c) [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FE-based strain observations at potential sensor locations alongside linear regression-based observation [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Corrosion-induced thickness loss (CITL) posteriors for six representative, randomly selected thickness loss [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Corrosion-induced thickness loss (CITL) posteriors for six representative, randomly selected thickness loss [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Corrosion-induced thickness loss (CITL) posteriors for six representative, randomly selected thickness loss [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Corrosion-induced thickness loss (CITL) posteriors for six representative, randomly selected thickness loss [PITH_FULL_IMAGE:figures/full_fig_p024_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Cumulative exceedance probability over the monitoring period for [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Thickness loss prior realisation histograms (orange) over the monitoring period and corresponding cumu [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Histograms and fitted log-normal probability distributions of thickness loss for (a) prior process realisations [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Expected reward-to-investment risk ratio as a function of the decision threshold mean value for different [PITH_FULL_IMAGE:figures/full_fig_p028_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Expected reward-to-investment risk ratio maps for different intrinsic cost assignments. [PITH_FULL_IMAGE:figures/full_fig_p029_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Relative risk-adjusted reward between strain-based monitoring strategies and inspection-only monitoring [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Relative risk-adjusted reward between strain-based monitoring strategies and inspection-only monitoring [PITH_FULL_IMAGE:figures/full_fig_p031_18.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript applies a Bayesian pre-posterior decision analysis to quantify the value of information (VoI) from strain-based structural health monitoring (SHM) for corrosion-induced thickness loss (CITL) in ship hull structures. Using a high-fidelity numerical model of a commercial vessel, the authors compare relative benefits of SHM versus traditional on-site inspections by defining consequence cost functions based on exceedance probabilities relative to a target CITL threshold that can be set by the decision-maker.

Significance. If the numerical model and exceedance-based cost functions are representative, the work supplies a quantitative framework for evaluating SHM benefits in maritime maintenance decisions and introduces a practical mechanism for incorporating decision-maker risk perception. This is a first application of VoI analysis to ship hull CITL monitoring and could support more informed adoption of monitoring technologies.

major comments (2)
  1. [Numerical model section] The central claim that the VoI framework quantifies relative benefits of strain-based SHM versus inspections rests on the high-fidelity numerical model correctly reproducing CITL statistics under operational conditions. The manuscript provides no description of model calibration to historical thickness measurements or validation against real ship data (see the numerical model section and any associated results tables).
  2. [Cost function and decision analysis sections] The consequence cost functions are defined via exceedance probabilities relative to a target threshold. The paper does not report elicitation of these functions from classification society rules, operator data, or explicit utility assessment, leaving open whether they faithfully encode real decision consequences (see the cost function definition and decision analysis sections).
minor comments (2)
  1. [Methods] Clarify the exact sensor noise model assumed for strain-based SHM and how it enters the pre-posterior analysis.
  2. [Results] Add a table or figure summarizing the specific monitoring strategies compared (e.g., sensor placement, inspection intervals) to improve readability of the results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We address each major comment below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Numerical model section] The central claim that the VoI framework quantifies relative benefits of strain-based SHM versus inspections rests on the high-fidelity numerical model correctly reproducing CITL statistics under operational conditions. The manuscript provides no description of model calibration to historical thickness measurements or validation against real ship data (see the numerical model section and any associated results tables).

    Authors: We agree that the manuscript does not provide explicit calibration of the numerical model to historical thickness measurements from the vessel or direct validation against real ship data. The model is a high-fidelity finite-element representation constructed according to standard naval architecture practices and literature on ship structural analysis; however, the study emphasis is on the VoI decision framework rather than on empirical model validation, which would require proprietary operational datasets not available to the authors. In the revised manuscript we will expand the numerical model section with additional detail on the model's formulation, boundary conditions, and material assumptions, and add a new subsection explicitly discussing model limitations and the value of future validation against real thickness-loss records. revision: yes

  2. Referee: [Cost function and decision analysis sections] The consequence cost functions are defined via exceedance probabilities relative to a target threshold. The paper does not report elicitation of these functions from classification society rules, operator data, or explicit utility assessment, leaving open whether they faithfully encode real decision consequences (see the cost function definition and decision analysis sections).

    Authors: The cost functions are deliberately formulated in terms of exceedance probabilities relative to a decision-maker-specified threshold precisely to allow flexibility and to encode risk perception without requiring a single fixed utility function. The manuscript therefore does not present a formal elicitation exercise from classification societies or operators. We acknowledge that anchoring the functions more explicitly to existing rules would improve practical relevance. In revision we will add references to relevant classification-society thickness-loss criteria and include a short discussion of how the framework can be instantiated with elicited utilities or operator-specific cost data. revision: partial

Circularity Check

0 steps flagged

No significant circularity; established VoI framework applied to new domain

full rationale

The paper applies a standard Bayesian pre-posterior decision analysis (VoI) to quantify benefits of strain-based SHM versus inspections for CITL in a high-fidelity vessel model. Consequence costs are defined via exceedance probabilities relative to a user-set threshold, which is an explicit modeling choice rather than a self-referential derivation. No equations or steps reduce reported VoI values to parameters fitted by the authors themselves or to self-citations that render the central result tautological by construction. The framework remains independent of the specific numerical model outputs and is presented as a first application to ship structures, with the derivation chain self-contained against external decision-analysis benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the accuracy of the numerical vessel model and the chosen cost functions; these are not derived from first principles but introduced to enable the decision analysis.

free parameters (1)
  • CITL threshold and exceedance cost function
    User-defined parameters that encode risk perception and directly scale the VoI calculation.
axioms (1)
  • domain assumption The high-fidelity numerical model faithfully reproduces real ship response to corrosion and strain monitoring.
    Invoked when applying the framework to the commercial vessel model.

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Reference graph

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