期刊
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
卷 22, 期 1, 页码 216-231出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/14759217221082007
关键词
Percussion method; convolutional neural network; fiber-reinforced polymer-concrete interface; damage virtualization; finite element modeling
This study addresses the detection of FRP-concrete interface damage using a fusion of percussion method and deep learning framework. It also provides visualization study and finite element modeling for further understanding the mechanical degradation caused by the fracture of underlying concrete. The results demonstrate the considerable application potential of this approach.
Reinforced concrete (RC) structures are commonly strengthened using externally bonded fiber-reinforced polymer (FRP) sheets. The bond between the FRP and concrete is a crucial factor affecting the strengthening effect, and debonding along the FRP-concrete interface is usually accompanied by the fracture of the underlying concrete. Therefore, it is necessary to identify the interface damage of FRP-to-concrete joints and conduct mechanical analysis. However, debonding is invisible damage that occurs inside the underlying FRP layer, which makes damage detection more difficult. To this end, this study fuses a percussion method with a deep learning framework to address the detection of such invisible lesions. Meanwhile, the visualization study provides guidance for later maintenance work. To further illustrate the hazard of the identified lesions, three-dimensional reconstruction for finite element modeling (FEM) with detected damage information based on percussion is proposed to elucidate the mechanical degradation caused by the fracture of underlying concrete. Lastly, the results of this study demonstrate that the detection, visualization, and FEM reconstruction of FRP-concrete interface damage using percussion signals has considerable application potential and is worthy of further study.
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