Machine Learning-Based Prediction of Atmospheric Corrosion Rates Using Environmental and Material Parameters

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R2 = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p < 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R2 = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation.