Prediction of Mass Loss Due to Atmospheric Corrosion in Power Transmission Towers Using an Image-Based Artificial Neural Network Approach

Conventional corrosivity assessment requires gravimetric measurements over exposure periods of at least one year (ISO 9223/9226), limiting rapid maintenance decisions for infrastructure. To address this gap, a predictive model for atmospheric corrosion-induced mass loss in AISI 1020 carbon steel and galvanized steel was developed using images analyzed through artificial neural networks (ANNs). The model was trained on RGB and HSV color-space images from salt-spray and field specimens. Three models were implemented: a YOLOv8-based segmentation network (ANN-1) to isolate corroded regions; a feedforward neural network (ANN-2) to predict mass loss from pixel color histograms; and an extended network (ANN-3) using meteorological variables to classify environmental corrosivity. The models estimated corrosion rates of 1204 ± 202 µm·year−1 for AISI 1020 carbon steel and 61 ± 44 µm·year−1 for galvanized steel in accelerated salt-spray tests, expressed as thickness loss per year, with low mean absolute errors and high R2 values. Applied to transmission towers and substations in Northeast Brazil, the predictions fell within experimental error margins—4.74 ± 1.60 g·year−1 predicted against 4.73 ± 1.61 g·year−1 measured per specimen for AISI 1020 carbon steel at the most aggressive substation. A graphical interface supports maintenance, material-selection, and surface-treatment decisions based on local corrosivity.