Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber‐ Reinforced Polymer‐Concrete

This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, utilizing multiple linear regression, an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), Multigene Genetic Programming (MGGP), and neural network models. The models were evaluated based on their accuracy. The optimal model identified was the GPR ARD model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. Analysis of this optimal model highlighted that the three most influential variables affecting the bond strength are the length of the FRP strip (Lf), the thickness of the strip ( tf), and the compressive strength of the concrete to which the strip is applied (fc'). Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios.