51313-02616-Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks

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Product Number: 51313-02616-SG

ISBN: 02616 2013 CP

Author: Swati Jain

Publication Date: 2013

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Stress corrosion cracking (SCC) continues to be a safety concern mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy stress external soil environment and the electrolyte chemistry beneath disbonded coatings. For these reasons assessing SCC failure probability at any given location on a pipeline is difficult. In addition the uncertainty in data makes the prediction of SCC challenging.
The complex interactions that affect SCC failure probability and the uncertainty can be modeled using Bayesian network models. The Bayesian network models link events by cause-consequence connections. The strengths of these connections are adjusted using expert knowledge analytical models and data from the field. A strong advantage of this model is the ability to update the connections or relationships between the variables from the field data is made available in time. In addition the model also allows for update of the information in time if the variables such as applied CP potential surface treatment operating conditions etc. are changed.
An approach to predict probability of High pH SCC failure using Bayesian networks was published in a previous publication; however the paper only detailed the part on stress effects. In this paper the previous model is extended to account for the variables such as soil type coating type applied CP potential etc. that affect the external electrolyte chemistry and the corrosion rate. Also the paper will focus on structuring these models to learn the relationships among the variables from the field information such as ILI data. This allows for the information from the data to be stored and used in future for assessment.