Understanding the depth and severity of corrosion is vital for evaluating the long-term durability and economic performance of Zn-based structures. In this study, a machine learning (ML) framework was applied to forecast the corrosion depth of zinc under varying environmental circumstances. A dataset consisting of 300 samples compiled from previously published atmospheric corrosion studies under various environmental conditions was used to develop and evaluate the machine learning models. Seven ML algorithms were developed by integrating different environmental constraints such as temperature, time of wetness (TOW), SO2 concentration, Cl− concentration, and exposure time as input parameters. The models were trained using cross-validation and hyperparameter optimization to ensure robust predictive performance and minimize overfitting. The Random Forest (RF) model confirmed superior predictive performance with an R2 of 96.4% and RMSE of 0.642 µm among all used models. The predictive ability of the optimized RF model was further confirmed using five new environmental systems, attaining excellent agreement with predicted values (R2 = 97.9%, RMSE = 0.87 µm). Model interpretability analysis using SHAP (SHapley Additive exPlanations) discovered that exposure time and SO2 concentration are the most significant parameters leading zinc corrosion behaviour. The developed ML framework provides interpretable insights into the influence of environmental parameters on atmospheric zinc corrosion behaviour and provides a reliable tool for forecasting corrosion depth. These findings highlight the potential of ML approaches to support corrosion mitigation strategies and accelerate materials design by reducing reliance on conventional trial-and-error experimentation.
Jain et al. (Fri,) studied this question.