Defect detection with estimation of material condition using ensemble learning for hammering test

Abstract

This paper introduces a new methodology of robotic hammering inspection for the maintenance of social infrastructures. In particular, the estimation of material defect conditions, such as delamination depth of concrete, is focused upon. Development of an automated diagnosis methodology is necessary for the maintenance of superannuated social infrastructures. The hammering test, which is an efficient inspection method, has attracted considerable attention in the context of automated inspection using robots. In this study, to apply the hammering test to robotic inspection, in which material conditions of infrastructures must be diagnosed in detail, an estimation method of the defect conditions is proposed, and an integration technique of plural classifiers for improving the inspection accuracy is introduced. Furthermore, an inspection system that can decrease the influence of the mechanical running-noise is implemented. Our experimental results using concrete test pieces demonstrate the effectiveness of the proposed method; the accuracy of the defect detection and defect condition estimation was validated.