Pitting Degradation Modeling of Ocean Steel Structures Using Bayesian Network

Abstract

Modeling depth of long-term pitting corrosion is of interest for engineers in predicting the structural longevity of ocean infrastructures. Conventional models demonstrate poor quality in predicting the long-term pitting corrosion depth. Recently developed phenomenological models provide a strong understanding of the pitting process; however, they have limited engineering applications. In this study, a novel probabilistic model is developed for predicting the long-term pitting corrosion depth of steel structures in marine environment using Bayesian network (BN). The proposed BN model combines an understanding of corrosion phenomenological model and empirical model calibrated using real-world data. A case study, which exemplifies the application of methodology to predict the pit depth of structural steel in long-term marine environment, is presented. The result shows that the proposed methodology succeeds in predicting the time-dependent, long-term anaerobic pitting corrosion depth of structural steel in different environmental and operational conditions.