Introducing of applicability semi-supervised learning to rebar corrosion judgement by hitting sound based on local outlier factor method

Abstract

The purpose of this study was to confirm the applicability of the local outlier factor method, which introduces semi-supervised learning to the non-destructive inspection using hitting to detect rebar corrosion inside a reinforced concrete specimen. An experimental study was carried out on RC specimens corroded by corrosion. we attempted to determine corrosion by LOF combined with clustering by the k-means method using the hitting sound spectrum of an RC specimen as an input. The proposed method is that training data of LOF is obtained by clustering a data group consisting of a large amount of unlabeled data and a small amount of negative labeled data, and extracting unlabeled data that can be regarded as negative based on which cluster the negative labeled data belongs to. As a result of the examination, the proposed method obtained judgment results that were roughly equivalent to supervised LOF, confirming its applicability to reinforcement corrosion judgment.