An experimental and statistical study on rebar corrosion considering the temperature effect using gaussian process regression

Abstract

Temperature is an important factor that affects corrosion potential in rebars. The temperature effect must be removed from the corrosion potential for precise measurement of corrosion rates. To separate the temperature effect from the corrosion potential, in this study rebar specimens were not embedded in concrete but, instead, were placed in an uncontrolled air environment. Gaussian process regression (GPR) was applied to the temperature and the non-corrosion potential data in order to remove the temperature effect from the corrosion potential. The results indicated that the corrosion potential was affected by the temperature. Furthermore, the GPR models of all the experimental cases showed high coefficients of determination (R2 > 0.90) and low root mean square errors (RMSE < 0.08), meaning that these models had high reliability. The fitted GPR models were used to successfully remove the temperature effect from the corrosion potential. This demonstrates that the GPR method can be appropriately used to assess the temperature effect on rebar corrosion.