Atmospheric corrosion is strongly influenced by the persistence of surface moisture, commonly quantified through the time of wetness (TOW). Traditional TOW models, such as those defined by ISO 9223, rely on fixed humidity and temperature thresholds and often fail to capture the complexity of real-world environmental conditions. This study introduces a novel TOW modelling approach based on the concept of damage accumulation supported by machine learning. Using environmental data from ten monitoring stations across New Zealand, 61 features were developed to simulate conditions conducive to electrolyte formation. Feature importance analysis identified three key predictors: two condensation-related humidity factors and cumulative solar radiation. A simplified corrosion model based on these three predictors achieved strong predictive performance (R² = 0.909). Bayesian linear regression confirmed the statistical relevance and directional influence of the selected factors. Notably, the Condensation Factor and the Dilution Factor demonstrated opposing effects on corrosion rate. The Condensation Factor, linked to early-stage condensation and the formation of concentrated electrolytes in droplets, showed a positive correlation with corrosion. In contrast, The Dilution Factor, associated with intense wetting events, exhibited a negative correlation, possibly due to the dilution of surface contaminants. The created model outperforms current ISO’s TOW model to predict atmospheric corrosion rate. This framework highlights the role of moisture in atmospheric corrosion and provides a foundation for future integration with corrodent-specific models. The proposed methodology offers a scalable and interpretable tool for improving corrosion rate predictions in diverse environmental settings.
Kunrath et al. (Thu,) studied this question.