A hybrid machine learning approach for predicting fiber-reinforced polymer-concrete interface bond strength

Abstract

The interfacial bond strength between fiber-reinforced polymer (FRP) sheets and concrete is crucial for structural design. This study presented a novel approach using ensemble learning models to predict bond strength and analyze input parameters' influence. No previous research used gene expression programming (GEP) for developing bond strength models in single shear tests. This research introduced GEP to develop an expression for estimating bond strength, comparing its performance with existing empirical models used in design codes.

Six ensemble machine learning models were tested: extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), categorical boosting (CatBoost), adaptive boosting (AdaBoost), random forest (RF), and explainable boosting machine (EBM), using 855 samples. CatBoost demonstrated superior performance with R2 = 0.98, RMSE = 1.61 kN, and MAE = 1.18 kN. The study utilized EBM's interpretability for parametric analysis through local and global explanations. Results showed FRP material and geometric properties had greater impact on bond strength than concrete properties. The novel GEP-developed empirical expression achieved higher accuracy compared to existing empirical models, with R2 = 0.812, RMSE = 4.63 kN, and MAE = 3.58 kN. The GEP model primarily relied on FRP's material and geometric properties, aligning with parametric analysis findings. Based on the results, both the CatBoost ensemble learning model and GEP model are recommended for estimating FRP-concrete interfacial bond strength.