Application of machine learning to digital hammering inspections

Abstract

In the future social infrastructure diagnosis for concrete structures such as bridges, tunnels and buildings, it is expected that the digital hammering inspection, which is objective, quantitative, and recordable to the conventional hammering inspection, will play a significant role. However, since there are a wide variety of defects due to poor construction and age-related deteriorations in the concrete structures, they have not been quantitatively identified enough by the digital hammering inspection in terms of their scales (size, depth from the surface, etc.). Also, establishing the data base of defects and deteriorations by mockup testing is not practical. As an application of machine leaning, an alternative model is proposed in this study that complements the experimental data of digital hammering inspections with the FEM analysis model and further expands the data by machine learning.