Data of AI Method on Hammering Sounds at Concrete Bridge
It is a popular to hammer sound test for visual inspection of deterioration in concrete structures. This convenient method is much effectiveness for expert engineers. However, it is difficult for young engineers and applicable to robotization to apply quantifying and to be systematic under consideraton to complicapable of relation of sound data and degree of deterioration. The factor of relation to hammering sound data and degree of degradation are not clearly. Development of quantifying and being systematic as engineering are the most important in practical business. In this study, it is expected to apply using AI technology. In this study AI is expressed by machine learning, in particular deep learning based on neural network. It makes a clearly for effectiveness of methods of machine learning and propose to apply to autoencoder.