A BASIC STUDY ON IMPROVING VERSATILITY OF THE RUST CONDITION RATING MODEL FOR WEATHERING STEEL BY USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

 Recently, convolutional neural network (CNN) based model for determining the rust condition rating model for weathering steel bridges has been proposed, and many studies have been carried out for practical application. The goal of this study was to improve the versatility of the rust condition rating model by diversifying the images of rust, which is the training data. The accuracy of the rust condition rating model by using CNN when trained on angled rust images was verified, and the versatility of the proposed model for rust images taken by inspectors under various conditions was investigated. The results showed that the accuracy of the proposed rust condition rating model increased by training rust images taken from various angles.