Development of Mapping Function to Estimate Bond–Slip and Bond Strength of RC Beams Using Genetic Programming

Abstract

Bond–slip is an important characteristic that determines the stiffness, displacement, and load-bearing capacity of a reinforced concrete (RC) beam. It is essential for performing a precise numerical analysis of the beam. In most cases, bond–slip models can define the bond–slip curve only when there are experimental data. However, many bond test data have been obtained from pull-out tests, and the dominant view is that the bond–slip behavior observed in the pull-out test is quite different from that in an actual RC beam. Therefore, a mapping function that makes it possible to estimate the bond–slip behaviors of beam specimens using those of pull-out specimens was developed in this study. A total of 255 pull-out specimen data and 75 beam specimen data were collected from previous studies, and the importance and influence of each feature of the two groups were analyzed using random forest and K-means clustering. The mapping function was derived using genetic programming, and its accuracy was verified through a comparison with existing models. The proposed model exhibits a high degree of accuracy in estimating bond–slip and bond strength in beam specimens and can provide useful information for understanding the difference in bond–slip behaviors between the two groups.