Prediction of FRP–Concrete Bond Strength Using a Genetic Neural Network Algorithm

The bond strength at the interface between fiber-reinforced polymer (FRP) composites and concrete is a critical factor affecting the mechanical performance of strengthened structures. To investigate this behavior, a comprehensive database of 1032 single-shear test results was compiled. A genetic algorithm-optimized backpropagation (GA-BP) neural network was developed using six input parameters: concrete width and compressive strength, and the FRP plate’s width, elastic modulus, thickness, and effective bond length. The optimized network, with a 6-13-1 architecture, achieved the highest prediction accuracy, with R2 = 0.93 and MAPE as low as 15.96%, outperforming all benchmark models. Eight existing bond strength prediction models were evaluated against the experimental data, revealing that models incorporating effective bond length achieved up to 35% lower prediction error than those that did not. A univariate sensitivity analysis showed that concrete compressive strength was the most influential parameter, with a normalized sensitivity coefficient of 0.325. The final trained weights and biases can be directly applied to similar prediction tasks without retraining. These results demonstrate the proposed model’s high accuracy, generalizability, and interpretability, offering a practical and efficient tool for evaluating FRP–concrete bond performance and supporting the design and rehabilitation of strengthened structures.