Abstract
Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.
Highlights
Corrosive environments for reinforcing steel bars in concrete structures may lead to severe deterioration
Controlling the corrosion of steel reinforcement has been a major concern for researchers and engineers. To deal with this issue, a number of studies have been focused on replacing steel rebars with fiber reinforcement polymer (FRP) bars [1,2,3]
Reports have shown that the bond strength predicted by the standard design equations such as the American Concrete Institute (ACI) model is a conservative estimation of the real values [11]
Summary
Corrosive environments for reinforcing steel bars in concrete structures may lead to severe deterioration. The bond strength between the FRP bars and concrete is one of the crucial factors in reinforced concrete structures. Over the past few decades, numerous studies have been conducted to determine the primary factors that affect the FRP bond behavior via either beam tests or direct pullout tests [5,6,7,8,9,10]. These studies have presented practical models for estimating the bond strength of FRP bars in concrete.
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