A GAN-Augmented Corrosion Prediction Model for Uncoated Steel Plates

Abstract

The deterioration and damage of aging steel structures have caused huge safety concerns. Corrosion has been identified as a big reason for the deterioration and damage, which causes steel members to lose materials. As a result, the structures’ stiffness and load-bearing capacity will be reduced, which brings economic losses and safety hazards. For the maintenance and repair of steel structures, fast and accurate prediction of corrosion development plays a critical role in numerical simulation analysis, which could save time and costs. In this research, we build a simulation system based on GAN data augmentation with UNet as the generator and MobileNetV2 as the discriminator. The goal is to effectively predict the corrosion behavior of uncoated steel structures over time and under different circumstances. The system can simulate three stages of corrosion based on the dataset collected from experiments. It can also predict the corrosion of steel plates in the next stage. The discriminator of the system can be used to classify the type of steel, the stage of corrosion, and days of corrosion. Based on comparative experiments, our system demonstrates outstanding performance and outperforms the baseline model.