Development and Implementation of Risk-Informed Decision-Making Framework to Manage Corrosion Threat in Gas Transmission Pipelines
Bibliographic record
Abstract
Abstract Quantitative Risk Assessment (QRA) is becoming an increasingly important part of asset integrity management in the pipeline industry, particularly for assets that can be internally inspected using magnetic flux leakage (MFL) in-line inspection (ILI) tools. The application of QRA to corrosion management involves performing probabilistic risk analysis using structural reliability methods which leverage ILI results, corrosion growth rates (CGR), and relevant sources of uncertainties to calculate the probability of failure (POF) and consequence of failure (COF). The risk quantified in terms of POF and COF, and then assessed against a quantitative reliability framework for each corrosion anomaly. One challenge arises when trying to make integrity decisions considering both the required prescriptive anomaly response criteria with the risk evaluated through QRA. It is common practice for operators to perform an annual QRA for an entire pipeline network, where anomalies found to be of higher risk are selected for risk-informed planning activities as part of the continuous improvement of integrity core process. This can usually happen after the prescriptive responses have been determined and, in many cases, the resulting repair programs have been started. This timeline disconnect in processes has the potential to lead to dig program inefficiencies and missing the opportunity to reduce even further the level of risk in a resource effective way. When compared to the Pipeline and Hazardous Materials Safety Administration (PHMSA) incident data [14] for natural gas pipeline operators, corrosion is the second most pervasive threat behind Third-Party Damage. This shows how industry-wide incorporation of QRA is becoming progressively more important for operators to optimize their risk-informed decision-making with respect to corrosion management. The purpose of this paper is to provide technical guidance on how to implement an integrated risk-informed decision-making (IRIDM) framework when combining QRA with prescriptive response criteria referenced in the Code of Federal Regulations (CFR) Title 49, Part 192 and the Canadian Standards Association (CSA) Z662:23 to enhance the safety and efficiency of a corrosion management program. The paper will then establish how the proposed integrated risk-informed decision-making framework performs when applied to a hypothetical population of corrosion anomalies on a North American natural gas pipeline network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".