ABSTRACT
Deterioration due to corrosion has a significant effect on the safety and reliability of many types of civil structures and infrastructures. Pitting corrosion is a localized accelerated dissolution of metal that occurs as a result of a breakdown of the otherwise protective passive film on the metal surface. Pitting corrosion of metals and alloys, which is critical to structural reliability, is difficult to deal with due to its complex nature Although great efforts have been made to understand the basic physical and chemical mechanisms of pitting corrosion, the mechanistic models have been still limited by large-scale randomness of pitting phenomena. In this paper, a gamma process-based hierarchical Bayesian model for characterizing pitting corrosion under inspection uncertainty is presented. The localized pitting corrosion was assumed to initiate at random times and the time-dependent depth growth was modeled by a gamma-process. The inspection uncertainty arising from imprecise measurement and imperfect detectability was also taken into account in this model. A Markov Chain Monte Carlo (MCMC) method was employed to estimate and update the uncertain model parameters. In a case study, the effectiveness of the proposed model was illustrated by estimating the actual number of pits and multiple pits depths within a corrosion system.
