CORROSION DETECTION FOR STEEL GIRDER BRIDGES USING A CONVOLUTIONAL NEURAL NETWORK

Abstract

It ia an urgent issue to introduce new technology for demanding efficient and low budget with saving lavor since the bridge inspection is increasing every year. In recent years, we have applied Convolutional Neural Network (CNN), which is one of the machine learning that has focused an attention on its use in the field of civil engineering. CNN is considered to be one of the highly effective method of the support for bridge inspection. In this study, we developed a learning model that is a corrosion detector for steel girder bridges using CNN as machine learning. Our learning models trained using the photographs of the results of road bridge inspections conducted by Fukushima Prefecture. The corrosion detector derived from our learning models as was validated by using test data from the photographs of the ground survey at the road bridge in service of Inawashiro, Fukushima.