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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. [ABSTRACT FROM AUTHOR]

The primary mode of failure at the interface between brittle engineering materials and fiber-reinforced polymers (FRP) is interface failure, making it essential to analyze the interface performance to ensure effective collaboration between these materials. Traditional experimental-based methods for interface fracture analysis are time-consuming and costly. This study introduces a deep learning model, "StrainNet," which generates strain distribution and bond-slip curves by learning the temporal variation of strain. These curves are used to evaluate the fracture performance of the interface between brittle engineering materials and FRP. To verify its accuracy, prediction models based on LSTM, GRU, and Basic GAN were established, and StrainNet demonstrated favorable performance compared to these models. StrainNet was applied to experimental test cases and FEM cases, producing strain distribution and bond-slip curves with low RMSE and MAPE values, indicating the model's robustness and generalization ability. Consequently, the developed model provides engineers and researchers with a quicker and more convenient method to obtain strain fields and interfacial constitutive laws, thus improving the design of FRP-reinforced structures. [ABSTRACT FROM AUTHOR]