Defect topology identification in concrete plates by machine learning based on self-attention using hammering response data

DOI
  • Shimada Masaya
    Department of Mechanical Engineering, Graduate School of Nagaoka University of Technology
  • Kurahashi Takahiko
    Department of Mechanical Engineering, Graduate School of Nagaoka University of Technology
  • Murakami Yuki
    Department of Civil Engineering, National Institute of Technology, Nagaoka College
  • Ikeda Fujio
    Department of Mechanical Engineering, National Institute of Technology, Nagaoka College
  • Iyama Tetsuro
    Department of Mechanical Engineering, National Institute of Technology, Nagaoka College

Bibliographic Information

Other Title
  • Self-attentionに基づく機械学習によるコンクリート床版打撃時の加速度応答を用いた内部欠陥のトポロジー同定
  • Effectiveness of data augmentation method for identified results
  • 同定結果へのデータオーギュメンテーションの効果

Abstract

The aging of concrete structures in Japan is becoming increasingly serious. Periodic inspection is necessary to prevent accidents caused by aging. One of the methods used to inspect concrete is the hammering test. In this research, we aim to develop a system to identify the topology of defects in concrete by machine learning based on the acceleration response data obtained from the hammering test. As a machine learning model, we build a neural network based on self-attention. Furthermore, we propose a data augmentation method for this task and test its effectiveness.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390012578706770944
  • DOI
    10.24806/rrnitnc.57.0_25
  • ISSN
    24323241
    00277568
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Allowed

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