Application of ensemble learning to defect detection of RC members using hitting sound

Abstract

The purpose of this study is to improve the performance of a defect detection method based on the Local Outlier Factor (LOF) using hitting sounds for surface-painted RC specimens. Bagging, which is a type of ensemble learning, is employed to build many weak learners from bootstrap samples. Each learner uses different frequency bands and k/N ratios selected randomly within a configured range, and this increases the variety and flexibility of the LOF model. The proposed method performs better than the standard LOF, even when the number of training samples is small or the surface is painted.