## 1 Introduction  
Rebar corrosion is a critical deterioration factor that is directly related to the service life of concrete structures. Rebars are protected by the passive film of concrete. However, when chloride (Cl\({}^{-}\)) or sulfate (SO\({}_{4}\)\({}^{2-}\)) penetrate into concrete, these aggressive materials destroy that passive film [1]. After that film is destroyed, rebars start to corrode. The critical defect is the volume expansion of the rebars which causes the concrete to crack [2]. Therefore, in the past, concrete researchers used an electrochemical method to detect corrosion, while currently the most representative methods are concrete resistivity method, half-cell method, and linear polarization resistance (LPR) method. Half-cell and concrete resistivity methods can produce results with simple measurements, making it possible to quickly determine the corrosion state of the rebar. LPR method needs an expert level of measurement skills and takes a long time to apply because it is affected by many factors such as temperature and relative humidity (RH) [3]. Recently developed corrosion detection methods utilize sensors and ultrasonic pulse velocity (UPV) [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The accuracy of detecting rebar corrosion is steadily increasing, but even with the technological progress, continuing research on the environmental factors related to rebar corrosion (e.g., humidity, temperature, impact) is essential [14]. In particular, temperature can readily increase for various reasons, such as seasonal climate change, equipment heat, friction, etc., and can directly affect rebar corrosion. Temperature is a main factor that affects rebar corrosion potential, but it is difficult to isolate or exclude the effect of temperature as most previous experiments had fixed temperature conditions [4; 5; 8; 12; 15; 16; 17]. Accordingly, many studies on the relationship between temperature and rebar corrosion potential are actively being carried out. For example, [PERSON] et al. [18] proposed a regression model for the prediction of corrosion rates with temperature effects. They assumed that temperature affects concrete resistivity and current density, and the results of their study showed a high coefficient of determination, indicating that temperature is an important factor for corrosion potential. However, as they did a traditional linear regression, large deviations in the data had to be accepted. Taking a different approach, [PERSON] et al. [19] studied the relationship between the passive film of stainless steel rebar and various temperature conditions (25, 35, and 45 \({}^{\circ}\)C). They also confirmed that temperature is an important factor in corrosion potential because the passive film weakened when the temperature was increased. Another study by [PERSON] et al. [20] checked the corrosion potential with heated rebar. The heated rebars showed a higher corrosion rate than the control specimen and the heat initiated a reaction between the rebars and unhydrated water. In addition, [PERSON] et al. [21] researched the effect of temperature on corrosion behavior with concrete pores and inner humidity of concrete. They found a low corrosion state at dry and high temperature conditions, but a high corrosion state at humid and high temperature conditions. Both of these research groups concluded that temperature is an important factor for corrosion potential. Statistical analysis is frequently used for assessing corrosion data. Additionally, complex data analysis skills including Bayesian methodology are required. Bayesian method is a fundamental part of machine learning (ML) in the artificial intelligence (AI) field, and is accordingly difficult to approach given its complex theory and calculations. However, corrosion research is increasingly using Bayesian method because enhanced computer performance (supercomputing) makes it possible to do complex calculations in a short time. In addition, the Gaussian process regression (GPR) method is coming to the fore. [PERSON] et al.