ESTIMATION METHOD FOR RUST CONDITIONS OF WEATHERING STEEL BRIDGES USING DEEP LEARNING BASED ON CAMERA IMAGES

Abstract

Recently, it is very important to reduce life cycle costs of bridges. Particularly, costs for prevention of the corrosion and rust comprise a large percentage of maintenance costs and the use of weathering steels for bridges has increased considering its cost-effectiveness. For example, the use of weathering steels is over 15% of new bridges in Japan. For maintaining of weathering steel bridges, estimation of the corrosion state is essential. The visual external inspection of the rust by skilled engineers is a common way for estimation of the corrosion. However, it is well known that there are some errors among the estimated results by different surveyors; hence, a simple and accurate evaluation method of the corrosion state is desired and deep learning has been paid attention. In this study, the convolutional neural network based on camera images about the rust visual inspection of weathering steel bridges is employed for quantitative estimation of the corrosion state. The results demonstrate that the proposed method can provide acceptable estimation of the rust conditions for practical application.