Standards and methods for dent assessment and failure prediction of pipelines: A critical review
Bibliographic record
Abstract
Dent, a common mechanical damage on pipelines, is associated with a significant local plastic deformation. Dents can cause pipeline failures, especially when they are combined with other types of defects such as gouges, fatigue, corrosion, and cracks. In this work, a systematic review of various assessment methods and standards for pipeline dents, including the combination of a dent with other defects, is conducted. Generally, the methods available today are not sufficiently accurate and reliable to assess pipeline dents, especially the dent-defect combinations. For plain dents on pipelines, both the depth-based criterion and the strain-based criterion are commonly used in engineering. Their main problems include inaccuracy and conservatism. For a dent combined with other defects, the existing assessment techniques are not mature enough to give reliable results. Both experimental testing and numerical modeling through finite element (FE) analysis are capable of investigating the influence of dents and dent-defect combinations on burst failure pressure of the pipelines, although an approximation to the reality is still the main difficulty existing in the experimental testing and FE analysis. Nowadays, relevant studies on assessment techniques for plain dents, a dent with fatigue and a dent with a single gouge have been common in literature. The combinations of a dent with corrosion or cracks have been rarely assessed due to complicated mechanisms involving a multi-physics coupling effect. Development of novel assessment methods by integrating mechanical stress and strain, electrochemical reactions and steel metallurgy will be a key topic to accurately assess the dent-defect combinations for improved pipeline integrity.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".