Server maintenance is scheduled for Saturday, December 21st between 6am-10am CST.
During that time, parts of our website will be affected until maintenance is completed. Thank you for your patience.
Use GIVING24 at checkout to save 20% on eCourses and books (some exclusions apply)!
Bayesian networks (BN) are useful tools for corrosion modeling. This paper is a case study demonstrating how to perform Internal Corrosion Direct Assessment (ICDA) using BN modeling with limited data. A BN model was developed for ICDA of a 50 km refined oil pipeline. Internal corrosion probability of failure along the pipeline was assessed.
We are unable to complete this action. Please try again at a later time.
If this error continues to occur, please contact AMPP Customer Support for assistance.
Error Message:
Please login to use Standards Credits*
* AMPP Members receive Standards Credits in order to redeem eligible Standards and Reports in the Store
You are not a Member.
AMPP Members enjoy many benefits, including Standards Credits which can be used to redeem eligible Standards and Reports in the Store.
You can visit the Membership Page to learn about the benefits of membership.
You have previously purchased this item.
Go to Downloadable Products in your AMPP Store profile to find this item.
You do not have sufficient Standards Credits to claim this item.
Click on 'ADD TO CART' to purchase this item.
Your Standards Credit(s)
1
Remaining Credits
0
Please review your transaction.
Click on 'REDEEM' to use your Standards Credits to claim this item.
You have successfully redeemed:
Go to Downloadable Products in your AMPP Store Profile to find and download this item.
Bayesian network modeling was explored as a solution to the challenge of effective use of large datasets, due to its ability to analyze complex cause-effect relationships while considering the variability and uncertainty in the data.
Stress Corrosion Cracking (SCC) is a serious threat to our pipeline infrastructure. Past SCC failures have shown that both NN pH SCC and high pH SCC may lead to catastrophic pipeline failure. This is due to the formation of cracks that are difficult to detect. Moreover, SCC is difficult to predict, as multiple mechanisms must interact to lead to the formation of these cracks.
Environmentally Assisted Cracking (EAC) of gas transmission lines constitute about 2.6% of the total number of significant incidents recorded in the U.S. Pipeline and Hazardous Materials Administration (PHMSA) database [1]. For the hydrocarbon liquid pipelines, the EAC-related incidents constitute about 1%. Although Stress Corrosion Cracking (SCC) incidents are a relatively small percentage of significant incidents, it is important to predict the location and rate of growth of SCC because of the potential for catastrophic consequences from the growth of undetected cracks.