Data-Driven Interpretable Machine Learning Prediction Method for the Bond Strength of Near-Surface-Mounted FRP-Concrete

Abstract

The Near-Surface-Mounted (NSM) technique for Fiber-Reinforced Polymer (FRP) strengthening is widely applied in the seismic retrofitting of concrete structures. The key aspect of the NSM technique lies in the adhesive performance between the FRP, adhesive layer, and concrete. In order to accurately predict the bond strength of embedded reinforced NSM FRP–concrete, this study constructs the relationship between the influencing factors of bonding performance and bond strength based on four machine learning (ML) algorithms: Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB). A unified and interpretable prediction method for FRP–concrete interface bond strength based on SHAP values and ML algorithms is proposed. The results indicate that the ML models exhibit good predictive performance, with the R2 of the test set ranging from 0.8190 to 0.9621, showing higher accuracy than empirical calculation formulas. Among them, the RF algorithm demonstrates the highest overall accuracy and optimal performance. Additionally, the SHAP (Shapley additional explanations) method quantitatively confirms that the width of the FRP strip has the most significant impact on bond strength. The newly developed hybrid ML model has the potential to become a new choice for accurately assessing the bond strength of NSM FRP strengthening technology.