Probabilistic Corrosion Impact Analysis Under a Changing Climate: A Numerical Model for Reinforced Concrete Structures

Abstract

Environmental factors play a critical role in the corrosion of reinforced concrete (RC) structures, directly impacting their durability, safety, and serviceability. Corrosion can lead to increased displacements, cracking, or even structural collapse, while also incurring significant economic costs. These issues are expected to intensify in certain regions due to the effects of climate change on corrosion mechanisms. In this study, the probability of steel depassivation was first estimated using climate variables—temperature, relative humidity, and CO₂ concentration—predicted by a machine learning model (Random Forest) trained on historical data. For the propagation phase, the present study employs an alternative Finite Element Method based on Positions (FEMP), using laminated frame elements. The corrosion effect of reduction of steel area was incorporated into the model to simulate long-term degradation of RC elements. Monte Carlo simulation was used to compute the failure probabilities. The proposed method was tested for various environmental conditions for RC structures placed in Brazil. The results demonstrate significant regional variation in depassivation times and failure probabilities, with nearly a 10% increase in SLS failure probability 60 years after depassivation. The study highlights the critical influence of macroclimatic variables on corrosion progression and structural reliability, suggesting that current design codes may not fully capture localized environmental effects.