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

DOI

Bibliographic Information

Other Title
  • 半教師あり学習を導入した局所外れ値因子法に基づく打音による鉄筋腐食判定の適用性

Abstract

<p>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.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390579599242139392
  • DOI
    10.11532/jsceiii.4.3_179
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

Report a problem

Back to top