Erosion depth prediction of chloride ions under stray current using FEM based CNNs

Abstract

Abstract Stray currents can accelerate the transport of corrosive ions, especially Cl−, in concrete materials, which is very detrimental to structural safety. Effectively predicting the erosion depth of Cl− is crucial for evaluating structural safety. This article is based on a finite element model and verifies the erosion depth of Cl− under different voltages, Cl− concentrations, and corrosion time through experimental data. A polynomial was used to fit the quantitative relationship between erosion depth, Cl− concentrations, and corrosion time under single voltage condition. However, this formula only applies to a single voltage and has too many parameters. Therefore, this article also established a CNNs regression model to predict the depth of Cl−, and the results showed the multiple regression ability of CNNs. It has been proven that CNNs can accurately predict the erosion depth, which helps to accurately evaluate structural safety. After comparing experimental values, CNNs, ResNet, and ResNet-attention, it was found that residual networks and attention mechanisms did not significantly improve the prediction accuracy of deep networks, which may be related to insufficient data volume. After expanding the dataset, ResNet performed the best overall, and ResNet-attention had better testing performance, which is related to the powerful feature extraction ability of the attention mechanism.