# A Weibull Distribution-Based Corrosion Rate Model for Intelligent Monitoring of Steel Structures in Marine Splash Zones

**Authors:** Quanfeng Ouyang, Jiahuan Rao, Chuanrui Guo

PMC · DOI: 10.3390/s26041401 · Sensors (Basel, Switzerland) · 2026-02-23

## TL;DR

This paper introduces a new corrosion model for steel structures in marine environments, using the Weibull distribution to enable real-time monitoring and safety prediction.

## Contribution

The novel contribution is a Weibull-based corrosion rate model validated for intelligent monitoring of marine steel structures.

## Key findings

- The model reveals a three-stage corrosion progression in marine splash zones.
- The Weibull model accurately captures time-variant corrosion behavior under varying splash intensities.
- Validation through lab tests confirms the model's reliability for real-time structural safety assessment.

## Abstract

Steel structures in marine splash zones (MSZ) experience severe corrosion owing to high humidity and frequent wet–dry cycles, which poses considerable threats to structural integrity and operational safety. To achieve intelligent, real-time corrosion monitoring, this study presents a corrosion-rate model based on the Weibull distribution, intended to serve as the core algorithm of smart corrosion sensors that continuously provide corrosion depth data via techniques such as electrochemical impedance spectroscopy or fiber optic sensing. The model was validated through systematic laboratory salt-spray cyclic tests that simulated MSZ conditions; corrosion behaviour was analysed by means of mass-loss measurements, electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The results reveal a three-stage corrosion progression and confirm that the Weibull model accurately captures the time-variant corrosion behaviour under different splash intensities. The model thus provides a reliable algorithmic foundation for intelligent corrosion monitoring, enabling real-time assessment of structural safety and prediction of residual life.

## Full-text entities

- **Diseases:** injury to (MESH:D014947), mass loss (MESH:C536030)
- **Chemicals:** O (MESH:D010100), steel (MESH:D013232), magnetite (MESH:D052203), Salt (MESH:D012492), silicate (MESH:D017640), NaCl (MESH:D012965), Pt (MESH:D010984), alpha-FeOOH (MESH:C094886), iron oxyhydroxides (MESH:C021024), Fe (MESH:D007501), water (MESH:D014867), chloride (MESH:D002712), ethanol (MESH:D000431), copper (MESH:D003300), iron oxides (MESH:C000499), diamond (MESH:D018130), Na (MESH:D012964), silica (MESH:D012822), ammonium citrate (MESH:C481046), Si (MESH:D012825), Fe3O4 (-), epoxy (MESH:D004853), acetone (MESH:D000096), Cl (MESH:D002713), beta-FeOOH (MESH:C473845), Mn (MESH:D008345)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Cell lines:** Q355 steel — Homo sapiens (Human), 5' 10' methylenetetrahydrofolate reductase deficiency, Finite cell line (CVCL_B3VP)

## Full text

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## Figures

14 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12944838/full.md

## References

39 references — full list in the complete paper: https://tomesphere.com/paper/PMC12944838/full.md

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Source: https://tomesphere.com/paper/PMC12944838