Bond–Slip Performance of Steel–Fiber-Reinforced Polymer Composite Bars (SFCBs) and Glass Fiber with Expansion-Agent-Reinforced Seawater Sea-Sand Concrete (GF-EA-SSSC) under Freezing–Thawing Environment

Abstract

The combined application of steel–FRP composite bars (SFCBs) and seawater sea-sand concrete (SSSC) in marine engineering not only solves the problem of resource scarcity and reduces the construction cost but also avoids the problems of chloride corrosion of steel reinforcement in seawater sea-sand concrete and the lack of ductility of FRP bars. At the same time, the addition of glass fiber (GF) and expansion agent (EA) in appropriate amounts improves the crack resistance and seepage resistance of concrete. However, the durability of SFCB with GF- and EA-reinforced SSSC in freezing–thawing environment remains unclear, which limits its potential application in cryogenic marine engineering. This study investigates the bonding properties between SFCB and GF-EA-SSSC interfaces using eccentric pullout experiments under different thicknesses of concrete protective cover and a number of freezing–thawing cycles. The results showed that the compressive strength and dynamic elastic modulus of SSSC decrease, while the mass loss increases with an increasing number of freezing–thawing cycles. Additionally, the bond strength and stiffness between SFCB and SSSC decrease, leading to an increase in relative slip. However, the rate of bond strength and stiffness loss decreases with an increase in the thickness of the concrete protective cover. Furthermore, formulas for bond strength, relative slip, and bond stiffness are established to quantify the effects of the thickness of the concrete protective cover and the number of freezing–thawing cycles. The experimental values obtained verify the accuracy of these formulas, with a relative error of less than 5%. Moreover, a bond stress–slip constitutive model is developed for SFCB and GF-EA-SSSC, and the fitting results closely resemble the experimental values, demonstrating a high level of model fit.