An integrated service life prediction model for chloride-induced corrosion in reinforced concrete structures

In recent years, there has been a growing amount of attention paid to the prediction of deterioration and the modeling of concrete structures service lifetime. The present study presents a novel multi-parametric model that combines the initiation and propagation phases of corrosion by combining Faraday's electrolysis principle with Fick's diffusion rule to forecast the chloride-induced corrosion service life of reinforced concrete. To increase reliability by 15–20%, this approach considers humidity effects (β), concrete resistivity (ρc), and supplementary cementitious materials, in contrast to other models (such as Life-365). Through experimental validation on 12 mixes with SCMs using RCPT, salt ponding, Half-Cell Potentiometer and gravimetric analysis, it was found that Fly Ash reduced corrosion rates by 25% and Ground Granulated Blast Furnace Slag decreased chloride diffusion by 40% when compared to Ordinary Portland Cement. In predicting marine exposure, the model outperformed Life-365 with an error of ±10% compared to field data. By optimizing material selection and cover thickness, engineers can save up to 30% on maintenance costs in maritime settings with a 40% GGBS specification. The objective of current study is to improve the model for localized corrosion prediction in complicated situations using probabilistic analysis and machine learning approaches.