Bayesian Updating and Reliability Analysis for High-pH Stress Corrosion Cracking in Gas Pipelines

Abstract

A new methodology for incorporating stochastic models in integrity maintenance management for high-pH stress corrosion cracking (SCC) in gas pipelines has been presented. The generation of new crack features along with the growth of existing ones are included in the stochastic modeling, by means of nonhomogeneous Poisson process (NHPP) and nonhomogeneous gamma process (NHGP), respectively. The dependence (correlation) among the growths of individual cracks is considered by employing the Gaussian copula method. Data from multiple in-line inspections (ILI) are used to evaluate the parameters of the stochastic models by means of Bayesian updating. Hence, a hierarchical Bayesian framework is developed that can efficiently account for the ILI associated measurement errors and the probability of detection (PoD) of the crack features. The Bayesian updating is performed through subset simulation, a structural reliability method (SRM) conjointly with the data augmentation (DA) technique. Multiple simulated crack features from different ILI inspections are employed for the implementation and validation of the methodology. At the end, the time-dependent system reliability is evaluated along with a parametric study that examines the impact of correlations among stochastic growths of crack features on the posterior growth model and system reliability.