Thumbnail Image
Publication or External Link
Alseyabi, Mohamed Chookah
Modarres, Mohammad
Pipelines are susceptible to degradation over the span of their service life. Corrosion is one of the most common degradation mechanisms, but other critical mechanisms such as fatigue and creep should not be overlooked. The rate of degradation is influenced by many factors, such as material, process conditions, geometry, and location. Based on these factors, a best estimate for the pipeline service life (reliability) can be calculated. This estimate serves as a guide for maintenance and replacement practices. After a long period of service, however, this estimate requires reevaluation due to the new evidence gathered from monitoring the conditions of the pipeline. Several deterministic models have been proposed to estimate the reliability of pipelines. Among these models is the ASME B31G code, which is the most widely accepted method for the assessment of corroded pipelines. However, these models are highly conservative and lack the ability to estimate the true life and health of the pipeline. In addition to the limitations embedded in these deterministic models are the problems with inspection techniques and tools that may be inadequate and susceptible to error and imprecision. What is needed, therefore, is a best-estimate assessment model that estimates the true life of these pipelines and integrates the uncertainties surrounding the estimate. Hence, this dissertation proposes a probabilistic model that is capable of addressing the limitations of these models (by accounting for model uncertainties), the inspection data (by characterizing limited and uncertain evidence), and subjective proactive maintenance (by involving the decision-making process under uncertainty). The objective of this research is to propose and validate a probabilistic model based on the underlying degradation phenomena and whose parameters are estimated from the observed field data and experimental investigations. Uncertainties about the structure of the model itself and the parameters of the model will also be characterized. The proposed model should be able to capture wider ranges of pipelines rather than only the network ones, so that the proposed model will better represent the reality and can account for material and size variability. The existing probabilistic models sufficiently address the corrosion and fatigue mechanisms individually but are inadequate to capture mechanisms that synergistically interact. Given that capturing all degradation mechanisms would be a challenging task, the new model will address two of the most important mechanisms: pitting corrosion and fatigue-crack growth. The field data is very limited, and the experiments required an extensive and expensive set-up before they could produce suitable results. Hence, relying primarily in the initial stage on the generic data available from the literature facilitated the construction of the empirical degradation model and provided an order-of-magnitude estimate of the parameters of the degradation model. The proposed model in it is simplest form has the capability to estimate the degradation outputs with the least parameter inputs available. The Bayesian approach was implemented to incorporate the experimental data to further improve the proposed model and estimate the two constants' values. The proposed empirical model can be used to estimate the aging life expended, which will enable inspection and replacement strategies to be developed.