## 1 Introduction  
In recent years, fiber-reinforced polymer (FRP) has seen extensive use in the externally bonded (EB) strengthening of concrete structures, owing to its advantages of a high strength-to-weight ratio, durability, and compatibility with concrete buildings [1]. However, prolonged service can lead to degradation in the performance of concrete structures strengthened with EB-FRP, particularly under harsh environmental conditions such as tidal conditions, which act as hygrothermal exposure and a salt attack. The combined effects of these parameters contribute to the uncertain evolution of the bond performance at the interface of EB-FRP-strengthened concrete [2]. Predicting the degradation of bond properties in EB-FRP-strengthened concrete is a significant challenge in engineering practice. Several researchers have investigated the interfacial bond performance of EB-FRP-strengthened concrete structures under hygrothermal conditions [3; 4; 5; 6; 7; 8; 9]. It has been observed that moisture adversely affects both the strength of the FRP itself and its interlaminar shear strength [3]. Furthermore, its bond performance is compromised due to the susceptibility of epoxy resins to the adverse effects of water and high temperatures [4; 5]. In engineering applications, aside from hygrothermal exposure, salt erosion emerges as another significant parameter contributing to the degradation of the interfacial bond performance of FRP-reinforced concrete. Studies indicate that concrete structures are susceptible to corrosive degradation by sulphate [10]. Although sulphate corrosion significantly impacts the bond performance of the FRP-concrete interface, it has minimal effects on the mechanical properties of the FRP material and adhesive [11]. Salt spray cycling environments have been shown to cause damage to concrete-adhesive interfaces [12]. Based on this observation, a bond-slip model for a carbon fiber-reinforced polymer (CFRP)-reinforced concrete interface under the influence of sulphate cycling has been proposed [13]. With the application of machine learning algorithms, research on the FRP-to-concrete interface's performance is no longer limited to experimentation. Employing data-driven methodologies to address these complex prediction challenges is a pragmatic approach [14; 15]. Researchers have advocated for the utilization of artificial neural network (ANN) models to forecast the strength of interfacial bonds, yielding superior predictive outcomes [16; 17; 18]. Additionally, a study has proposed an equation for interfacial bond strength that utilizes an ANN model based on artificial bee colony optimization [19]. Building upon this foundation, other researchers have introduced an innovative and interpretable machine learning technique to predict the interfacial bond strength of FRP concrete by integrating machine learning models with traditional physical models [20]. However, the majority of research, whether using experimental methods or machine learning methods, predominantly concentrates on assessing the durability of FRP-to-concrete interfaces with respect to a singular environmental factor [21; 22]. There is a scarcity of research considering the coupling effect of environmental factors. This is because when considering the coupling effects of multiple factors, experimental design often becomes more complex and the scale larger. Additionally, due to limited experimental data, traditional machine learning methods, which rely on extensive databases, cannot be effectively applied. However, for complex studies involving numerous influencing variables, few-shot learning, transfer learning, and meta-learning methods offer viable approaches [23]. Some researchers have accomplished the prediction of square steel bar bond-slip curves by utilizing experimental data on the bond-slip of circular-steel-reinforced concrete as prior knowledge and a small amount of experimental data on the bond-slip of square-steel-reinforced concrete as posterior knowledge [24].