Structural Monitoring and Maintenance

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1-D CNN deep learning of impedance signals for damage monitoring in concrete anchorage

  • Quoc-Bao Ta (Department of Ocean Engineering, Pukyong National University) ;
  • Quang-Quang Pham (Department of Ocean Engineering, Pukyong National University) ;
  • Ngoc-Lan Pham (Department of Ocean Engineering, Pukyong National University) ;
  • Jeong-Tae Kim (Department of Ocean Engineering, Pukyong National University)
  • Received : 2023.01.23
  • Accepted : 2023.03.10
  • Published : 2023.03.25
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Abstract

Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.

Keywords

Acknowledgement

This work was supported by a Research Grant from Pukyong National University (2021-2023).

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