Experimental Identification and Grading of Corrosion Defects in High-Tensile-Strength Steel Wires of Suspension Bridge Main Cables Based on Deep Learning

The primary load-bearing structures of suspension bridges are the main cables, which are constructed with high-tensile-strength steel wires. Throughout the service life of a suspension bridge, the main cables not only endure cyclic loading from various loading sources but also from severe environmental conditions. These long-term applied loading conditions may result in significant deterioration of material characteristics and potentially cable failure, compromising both the longevity and security of the suspension bridge. Thus, analyzing corrosion patterns based on the main cables’ high-tensile-strength steel wires and evaluating their corrosion intensity are critically important for civil engineers. This paper utilizes a copper-accelerated salt spray test to fast generate samples of four distinct corrosion stages of steel wires. By employing the semantic segmentation model Deeplabv3+, the corrosion positions can be determined. By utilizing three image classification models—ResNet50, ShuffleNet, and DFL (Discriminative Filter Bank Learning), the stages of corrosion in samples were classified and analyzed as a reference for engineering applications.