Quantitive Assessment of Corrosion Probability—A Bayesian Network Approach

Corrosion processes mainly affect the probability of failure, which then leads to consequences, such as, fire, explosion, or environmental damage. This paper focuses on the use of Bayesian network models for assessing the probability of corrosion. The Bayesian network approach incorporates cause- effect relationships of complex systems in the form of conditional probabilities. This method considers both knowledge uncertainties (i.e., modeling uncertainties) and data uncertainties to make more informed decisions. The Bayes theorem allows the model to predict the probability of events from their causes, and, if a particular event is known to have occurred, predict probable causes of that event. Two case studies, the first one involving internal corrosion and the second involving external corrosion of oil and gas pipeline, are presented, along with validation using field measurements. The extension of the approach to predicting stress corrosion cracking of pipelines is discussed.