| Title |
Acoustic Emission-Based Damage Monitoring for Compressive Failure of Concrete Laterally Confined with FRP Sheet |
| Authors |
정석훈(Jeong Seok-Hun) ; 김연우(Kim, Yeon-Woo) ; 천우진(Cheon, Woo-Jin) ; 정성훈(Jeong, Seong-Hun) ; 엠 후자이파(M. Huzaifa) ; 서수연(Seo, Soo-Yeon) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.9.339 |
| Keywords |
Acoustic Emission Technique; Concrete Block; Fiber Reinforced Polymer (FRP) Sheet; Lateral Confinement |
| Abstract |
The purpose of this study is to evaluate the effectiveness of CFRP confinement in concrete and to examine the relationship between
load-strain behavior and acoustic emission (AE) characteristics during monotonic compressive loading. Five concrete specimens with different
CFRP reinforcement configurations were tested under axial compression, while strain and AE signals were measured simultaneously in real
time. The results show that increasing the number of CFRP layers significantly enhances both compressive strength and ductility. Load-strain
curves indicate that CFRP sheets effectively restrain the lateral expansion of concrete, thereby improving confinement performance. AE
analysis demonstrates that cumulative AE events and absolute energy rise sharply near failure. In addition, the b-value increases with higher
reinforcement levels, suggesting suppression of large crack formation and greater dispersion of microcracks. To improve the accuracy of
damage localization, machine learning models were trained using pencil lead break calibration data to predict AE source coordinates. The
trained models achieved approximately 80 percent accuracy, and the predicted AE source locations correspond closely with observed crack
patterns. These findings confirm that CFRP confinement improves compressive performance and demonstrate that machine learning-based AE
source localization is a reliable method for damage monitoring and failure assessment of CFRP-confined concrete. |