Improved Semi-Quantitative Reliability-Based Method for Assessment of Pipeline Dents With Stress Risers
Bibliographic record
Abstract
Abstract Dents, especially those interacting with stress risers, can pose integrity threats to pipeline systems. Regulations in Canada and the United States mandate the repair of dents based on depth and interaction with stress risers, however, there have been cases in the past where dents that have passed these criteria have ended up in loss of containment. Recent industry’s recommendations regarding dent integrity analysis are predominantly based on strain, and the dent-fatigue models have been proven to be limited in their application. Additionally, these models or methodologies are generally deterministic which may not fully account for uncertainties associated with pipe properties and in-line inspection (ILI) tool measurement. Enbridge Liquid Pipelines Inc. had previously presented a framework to support system wide dent assessment with an efficient probabilistic-based calibrated semi-quantitative analysis method for dents (SQuAD), which elicits potentially injurious features from thousands of features within a system in a reasonable analysis timeframe. This paper expands on the authors’ previous work and presents several improvements that have since been made to the SQuAD model to address the limitations in the initial version of the model. The previous version of SQuAD was strain-based and did not explicitly account for pressure-cycling induced, fatigue-based failure quantitatively. An approximate circle fitting method was adopted for estimating the dent’s radii of curvature in order to calculate strains. In the improved model, filtering techniques have been employed to reduce the noise in the ILI-reported data while preserving the dent shape. Furthermore, a simplified FEA process has been developed to calculate the stresses within a dent due to pressure cycles, thus the fatigue-based Probability of Failure (PoF) of a dent can now be estimated using S-N approach. The filtered data allows for better accuracy in quantifying the radius of curvature of dents as reported by ILI tools, which are used for calculating dent strain as recommended in the updated version of ASME B31.8, Appendix R. Finally, the feasibility of applying this improved SQuAD model is discussed from an operator’s perspective. The improvements allow the enhanced SQuAD model to be used as an effective screening tool on a system-wide basis as part of a comprehensive, reliability-based dent assessment framework.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".