Bibliographic record
Abstract
Abstract Dents in pipelines, especially when associated with crack formation, pose a significant pipeline integrity threat. With crack formation during the initial indentation process, the fatigue life and failure pressure can dramatically reduce compared with crack-free dents. Over the past decade, cracks associated with the dent-formation process were typically assessed using strain-based engineering critical assessment (ECA), which compares the dent strain and curvature measured by in-line inspection (ILI) geometry tool, with industry-established damage criteria. However, in recent dent management practice, TC Energy has observed cracks in several shallow dents with strain levels much lower than any established fracture criteria. Cracks were found at some dents with measured depth at only 1.3% outer diameter (OD), and the calculated strain with ASME B31.8 nonmandatory method was ∼4% [6]. It is believed that the strain-based assessment solely based on post-formation shape measured by ILI geometry tool likely requires improvement to capture additional aspects that might contribute to formation of dent-cracks. Various dent formation processes were reviewed, and the multiple potential contributing factors are discussed in this paper, including loading rate sensitivity, temperature effect, stress triaxiality and geometrical influence. The effects of these factors on material responses, dent-crack formation and fracture damage are outlined. Observed gaps in the current strain-based dent assessment process are also outlined, and a multi-element approach to assess dents and identify dent-induced cracks is proposed, which includes evaluation of combined Caliper, magnetic flux leakage (MFL), electromagnetic acoustic transducer (EMAT) and axial flaw detector (AFD) data, enhanced dynamic testing and ductile failure damage indicator (DFDI) determination, materials modeling and finite element analysis (FEA) techniques. Results of two case studies using multiple real-life dent features are described to illustrate effectiveness of the proposed approach.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".