Effect of dry and wet conditions of concrete on hammering sound judgment using local outlier factor method for damage of RC beam

Abstract

In recent years, research such as digitizing the hammering sound and analyzing it using machine learning to identify the damages, etc is active. However, many studies have focused on the determination under ideal conditions in the laboratory, and so the changes in environmental conditions, for example dry and wet conditions of concrete, have not been sufficiently investigated. Therefore, in this study, examined whether it is possible to determine the damage of concrete specimens (RC beams) based on the dry and wet conditions using the local outlier factor method, which is one of the machine learning methods. Specifically, the authors collected sound data from RC beam specimens before and after loading in both dry and wet conditions, and used these data as input for an experimental study. As a result, it was confirmed that there is a possibility that the judgment can be made regardless of the dry or wet condition, depending on the input data.