Probabilistic Corrosion Initiation Model for Coastal Concrete Structures

Corrosion of the reinforced concrete (RC) structures has been affecting the major infrastructures in U.S. and in other continents, causing the recent several bridge collapses and incidents. While the theoretical understanding is well-established, the reliable prediction of the corrosion process in the RC structural systems has hardly been successful due to the inherent uncertainties existed in the electrochemical corrosion process and the associated material and environmental conditions. The paper proposes a computational framework to develop evidence-based probabilistic corrosion initiation models for the reinforcing steels in the RC structures, which predicts the corrosion initiation time and quantifies the inherent variances considering various acting parameters. The framework includes: probabilistic modeling with Bayesian updating based on the sets of previously generated experimental data; Bayesian model/parameter selection considering various parameters, such as material properties and environmental conditions; corrosion reliability analyses to predict the probabilities of the corrosion initiation at given time t, structural configurations, and environmental conditions; and sensitivity analyses to measure and to rank the influences of each acting parameter and its uncertainty to the probabilities of the corrosion initiation. Total of 284 sets of experimental data exposed to the coastal atmospheric environments are used for the modeling. The goal of the Bayesian model selection presented in this paper is to obtain the most accurate and unbiased model using the simplest form of expression. The developed example corrosion model is currently limited to the initiation of diffusion-induced corrosion. The model can be updated, improved, or modified upon future available sets of data. The research contributes to the decision making to improve the corrosion reliability, corrosion control, and further the structural reliability of corroding structures.