Pipeline deterioration arises chiefly as the result of various types of internal and external corrosion processes, which are typically subject to several uncertainties. They include material uncertainties, uncertainties in external influences such as loading and environmental variations, uncertainties in operating conditions, various spatial and temporal uncertainties, inspection uncertainties, and modeling uncertainties. Typically, the metal loss time-path at one defect feature may be quite different from the metal loss time-path in a neighboring location even when subject to supposedly similar loading, material and environmental circumstances. On top of that, in-line inspections (ILI) of pipeline systems affected by deterioration are performed infrequently and suffer from considerable uncertainty due to sizing errors and detectability. The present paper provides a Hierarchical Bayes framework for corrosion defect growth. While a full Hierarchical Bayes analysis is practical only for selected critical defect features, we also develop a simplified method based on multi-level generalized least squares. The latter method is useful for scanning large defect inspection data sets. Two detailed examples of the approach are presented.

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