EFFECT OF HITTING CONDITIONS ON JUDGEMENT FOR RE-BAR CORROSION CRACKS BASED ON NEURAL NETWORK USING HAMMERING SOUNDS

Abstract

Some recent studies have been conducted to detect the deterioration status of concrete structures based on AI technologies by hammering sounds. The focus of this study is to examine the effects of hitting conditions of training data and test data used in judgment for re-bar corrosion cracks using AI technology on the judgment results. For this purpose, a hammering sounds test with the RC specimens was conducted to determine the corrosion cracks by using a neural network, the effects of the difference and the range of variation in maximum impact force on the judgment results were considered. As a consequence, the effects were shown that the harder the maximum impact force, the better the accuracy when the maximum impact force at the time of hammering is coincident for training data and test data. In addition, it was confirmed that the accuracy tends to decrease in the case of the maximum impact force of both data is discrepant and in the case of the range of variation in the maximum impact force is wide. It hence has become clear that it is desirable to be harder the maximum impact force, to narrow the range of variation, and to make the maximum impact force between training data and test data approximately coincident at the time of recording hammering sounds.