Artificial Neural Network-Based Modeling of Atmospheric Zinc Corrosion Rates Using Meteorological and Pollutant Data

Understanding the depth and severity of corrosion is crucial for predicting the long-term durability and economic viability of Zn-based structures. This study investigates the relationship between meteorological and pollution parameters on the corrosion rate of zinc using an artificial neural network (ANN) model trained on global data. The model incorporates temperature, time of wetness (TOW), SO2 concentration, Cl− concentration, and exposure time as input variables, with corrosion depth as the output. The ANN model demonstrated high predictive accuracy, achieving correlation coefficients of 0.99 and 0.95 for the training and test datasets, respectively, indicating strong agreement with the experimental data. A graphical user interface was developed to facilitate the practical application of the model. Sensitivity analysis using the index of relative importance (IRI) identified the SO2 concentration and TOW as the most influential factors, emphasizing their critical role in zinc corrosion. These findings enhance our understanding of the Zn corrosion dynamics and provide valuable insights into corrosion prevention strategies. A user-friendly graphical user interface (GUI) was developed using Java, enabling accurate prediction of the corrosion depth in zinc with approximately 95% accuracy without requiring prior knowledge of neural networks or programming.