Imaging-Based Rating for Corrosion States of Weathering Steel Using Wavelet Transform and PSO-SVM Techniques

Abstract

Weathering steel with a natural corrosion-resistant feature has been widely applied to the structural components of steel bridges. However, severe surface corrosion damage has been frequently observed in the weathering steels of bridges, which causes the performance degradation of the structure. Conventional visual classification approaches are time-consuming and subjective and cannot provide quantitative evaluation effectively and efficiently. This paper presents a new imaging-based intelligent method for quantitatively rating the corrosion states of weathering steel bridges. Images are characterized by image texture analysis using two-dimensional wavelet decomposition, from which both the local and global energy distributions of each detail subimage are extracted as representative features. To enhance the performance of a support vector machine (SVM) in corrosion state classification, a particle swarm optimization algorithm (PSO) is developed to obtain the optimal parameters of the SVM. A comparative study indicates that PSO-SVM can achieve better classification accuracy rates than artificial neural network. Numerical results demonstrate that this study provides an effective approach to imaging-based rating by integrating wavelet transform and PSO-SVM techniques for allocating the condition state of corroded weathering steel.