Probabilistic Load-bearing Capacity Estimation of RC Beams Using Corrosion Crack Distributions Obtained by UAV and Machine Learning

DOI
  • ADACHI Takehiro
    早稲田大学大学院 創造理工学研究科建設工学専攻
  • YAMADA Taiki
    早稲田大学大学院 創造理工学研究科建設工学専攻
  • NAKAMURA Satoru
    早稲田大学大学院 創造理工学研究科建設工学専攻
  • XU Zhejun
    早稲田大学大学院 創造理工学研究科建設工学専攻
  • NAITO Hideki
    東北大学 大学院工学研究科 土木工学専攻
  • AKIYAMA Mitsuyoshi
    早稲田大学 創造理工学部 社会環境工学科

Bibliographic Information

Other Title
  • ドローンを用いて取得した腐食ひび割れ情報と機械学習モデルを用いた劣化RCはりの確率論的耐荷力評価

Abstract

<p>Instead of visual inspection, it is now possible to conduct inspections based on images taken by an UAV. However, the current inspection using the UAV is limited to the identification of cracks, and their images have not yet been used to evaluate the structural performance, especially the load-bearing capacity. Since corrosion cracks due volumetric expansion of iron oxide appear on the surface of reinforced concrete (RC) structures in chloride-laden environments, it would be possible to evaluate the load-bearing capacity of deteriorated RC members if the amount of rebar corrosion inside the RC members can be estimated using images of these cracks taken by the UAV. In this study, probabilistic load-bearing capacity of deteriorated RC members is evaluated using information on the width of corrosion cracks obtained by UAV photography, in addition to finite element analysis, stochastic field theory, and machine learning. The UAV images are more difficult to identify corrosion cracks than the close-up images, and their effects on the estimated load-bearing capacity of RC members are investigated.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390016649288715008
  • DOI
    10.11532/jsceiii.4.3_10
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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