Forecasting bond strength of various FRP bars with different surface characteristics in concrete using machine learning models

Fiber-reinforced polymer (FRP) bars are gaining prominence in civil infrastructure due to their high strength-to-weight ratio, corrosion resistance, and low thermal conductivity. The bond strength (BS) between FRP bars and concrete, which is influenced by surface treatments like sand-coating or ribbing, plays a critical role in ensuring structural performance and durability. This study aims to predict the bond strength of different FRP bar types and surface characteristics in concrete using machine learning models. A total of 416 datasets from standard pull-out tests were collected and statistically analyzed, considering variables such as bar type, surface treatment, concrete compressive strength, bar diameter, bonded length, concrete cover, and FRP bar tensile properties. Two machine learning models, Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) were developed for bond strength prediction. Model performance was evaluated using Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Scatter Index (SI). XGBoost demonstrated superior performance with lower RMSE and SI, and higher R values in 5-fold cross-validation. Sensitivity analysis identified concrete compressive strength as the most significant input in bond strength prediction. Additionally, main effect plots and Analysis of Variance (ANOVA) tests were conducted to further investigate the relationships between variables. These findings contribute to a more accurate understanding of FRP bar-concrete interactions, facilitating the optimization of structural design.