NDLI: Development of a Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks: A Concept

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Jain, Swati Ayello, Francois John, A. Beavers Sridhar, Narasi
Copyright Year	2012
Abstract	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 an attempt to assess the SCC probability, a Bayesian network model was created. The model links events by cause-consequence connections. The strengths of these connections are adjusted using expert knowledge, analytical models, and data from the field. Bayesian network modeling was chosen because it takes into account the high degree of uncertainty in the input parameters. Other models have been developed to assess SCC: such as indexing methods, heuristics models, and mechanistic models. However, their main limitation is the uncertainty of the input parameters. One other strength of the Bayesian model is that calculations can be run in two directions: the forward direction from cause to consequence and the backward direction from observation to causative factors. In the forward direction, the model evaluates the probability of SCC failure using various input probabilities of factors that are important to SCC. In the backward direction, the model can evaluate the effect of any known occurrence of SCC failure on the probabilities of causative factors and thus condition the Bayesian network to evaluate the future failure probability. In this paper, we discuss a Bayesian network model for high-pH SCC. The conceptual framework, acquisition of data, and the inclusion of uncertainties are described. In addition, an example of the model application to high pH SCC is given. The effects of service and field conditions such as soil type, soil chemistry, coating type, surface preparation techniques, stresses, residual stress due to pipe manufacturing conditions, welds, dents, location such as proximity to rivers, wetting and drying cycles, etc. on the SCC probability can be assessed with the model. The model details shown in this publication will only cover the stress affect due to surface preparation, welds, dents, and manufacturing conditions and temperature effect. The effects of other factors and validation against field experience will be discussed in future publications.
Sponsorship	International Petroleum Technology Institute Pipeline Division
Starting Page	615
Ending Page	625
Page Count	11
File Format	PDF
ISBN	9780791845158
DOI	10.1115/IPC2012-90340
Volume Number	Volume 4: Pipelining in Northern and Offshore Environments; Strain-Based Design; Risk and Reliability; Standards and Regulations
Conference Proceedings	2012 9th International Pipeline Conference
Language	English
Publisher Date	2012-09-24
Publisher Place	Calgary, Alberta, Canada
Access Restriction	Subscribed
Subject Keyword	Cycles Uncertainty Temperature Wetting Pipelines Stress corrosion cracking Coatings Modeling Welded joints Underground pipelines Surface preparation Manufacturing Safety Failure Probability Network models Rivers Stress Indexing (machining) Chemistry Soil Electrolytes Metallurgy Pipes Drying
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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