This paper presents two artificial intelligence techniques (e.g., gene expression programming (GEP) and random forest (RF)) for predicting the bond strength of near-surface-mounted (NSM) fiber-reinforced polymer (FRP) strips or rods bonded to concrete. Experimental data from 145 direct pullout tests collected from the literature and five parameters, namely, the bond length, FRP axial rigidity, groove depth-to-width ratio, epoxy tensile strength, and concrete compressive strength, were used to develop the GEP and RF models. A comparison was conducted between the proposed GEP and RF models and two existing empirical models, namely, Seracino’s model and Zhang’s model, and six statistical indices were used to evaluate the performance of these four models. The results show that the proposed GEP and RF models had higher coefficient of determination (R²) values and lower root mean squared error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), mean absolute percentage error (MAPE), and integral absolute error (IAE) values than the two existing empirical models. Finally, a detailed parametric study was conducted to investigate the influence of each input variable on the bond strength. The results showed that the bond strength increased with increasing bond length, FRP axial rigidity, groove depth-to-width ratio, and concrete compressive strength, while the epoxy tensile strength had little effect on the bond strength.

本文介绍了两种人工智能技术（例如，基因表达编程（GEP）和随机森林（RF）），用于预测粘结到混凝土上的近表面安装（NSM）纤维增强聚合物（FRP）条或棒的粘结强度。 。利用GEP和RF开发了从文献中收集的145个直接拉拔试验的实验数据和五个参数，即粘结长度，FRP轴向刚度，槽深宽比，环氧抗拉强度和混凝土抗压强度楷模。在提议的GEP和RF模型与两个现有的经验模型（Seracino模型和Zhang模型）之间进行了比较，并使用六个统计指标评估了这四个模型的性能。结果表明，提出的GEP和RF模型具有更高的确定系数（R²）值和比两个现有经验模型更低的均方根误差（RMSE），均值绝对误差（MAE），均方根相对平方误差（RRSE），均值绝对百分比误差（MAPE）和积分绝对误差（IAE）值。最后，进行了详细的参数研究，以研究每个输入变量对粘结强度的影响。结果表明，粘结强度随粘结长度，FRP轴向刚度，槽深宽比和混凝土抗压强度的增加而增加，而环氧拉伸强度对粘结强度的影响很小。