Techno Press
You logged in as Techno Press

Structural Monitoring and Maintenance
  Volume 10, Number 1, March 2023 , pages 43-62

1-D CNN deep learning of impedance signals for damage monitoring in concrete anchorage
Quoc-Bao Ta, Quang-Quang Pham, Ngoc-Lan Pham and Jeong-Tae Kim

    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.
Key Words
    1-D CNN; anchorage zone; concrete damage; convolutional neural network (CNN); damage classification; deep learning; impedance-based monitoring; smart aggregate
Quoc-Bao Ta, Ngoc-Lan Pham and Jeong-Tae Kim: Department of Ocean Engineering, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea
Quang-Quang Pham; Bridge and Road Department, Danang Architecture University, Da Nang 550000, Vietnam

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2023 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: