Mechanoluminescent Testing as an Efficient Inspection Technique for the Management of Infrastructures

  • Yoshida Akihito
    Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
  • Liu Linsheng
    Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
  • Tu Dong
    Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology
  • Kainuma Shigenobu
    Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology Department of Civil Engineering, Kyushu University
  • Xu Chao-Nan
    Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology International Institute for Carbon-Neutral Energy Research (WPI-I<sup>2</sup>CNER), Kyushu University

この論文をさがす

抄録

<p>This paper reports on the mechanoluminescence inspection technology we have developed and its applications. The inspection technology is expected to identify deterioration and damage, such as fatigue cracks developed on steel members of steel structures, using particular mechanoluminescence (ML) phenomenon. In field testing at an urban highway bridge currently in service, fatigue cracks in steel box girders were successfully detected using the proposed technology. In addition, using a conventional crack detection method known as magnetic particle inspection (MT), similar results were obtained in terms of crack judgment, suggesting that the reliability of the ML method is equivalent to that of the MT method. An advantage of the ML inspection method is that it does not require removing corrosion protection coating, saving labor that is necessary in the MT method. The field testing also examined the possibility of evaluating precautionary measures (repair) as another application of the ML technique. As a result, the ML technique quantitatively evaluated that detected cracking had been properly repaired (removed). It is expected that the ML technique will contribute to effective maintenance and management of infrastructures from the perspective of preventive maintenance.</p>

収録刊行物

被引用文献 (5)*注記

もっと見る

参考文献 (13)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ