Methodologies for Establishing the Probability of Pipeline Failure at Slope Crossings
Bibliographic record
Abstract
Pipelines in transmission pipeline networks often traverse land slopes along the right-of-way; especially near water crossings. While the vast majority of these slopes are stable, some might have a potential for instability related movements. Accordingly, pipelines subjected to these movements are susceptible to strain overload which may cause loss of containment in terms of buckling and/or tensile elongation failure modes. In order to analyze the risk of failure of pipelines due to slope movement it is beneficial to establish probabilistic approaches that can predict the likelihood of failure at each site given both aleatory and epistemic uncertainties. Estimation of such likelihood would support prioritization of integrity mitigation actions and confirm pipelines’ safety. There is a gap in pipeline literature in terms of available probabilistic approaches to analyze, assess, and manage such an integrity threat. Two probabilistic approaches are presented herein; a qualitative ranking analysis of slope hazards (QuRASH) and a semi-quantitative analysis of slope hazards (SQuASH). QuRASH is a qualitative approach that adopts site scores based on available slope characteristics, historical movements, expert opinion, and mitigation strategies. SQuASH is a reliability-based explicit limit state approach. Both approaches were applied to a large simulated sample of slope crossings that exhibit characteristics representative of North America transmission pipeline slope crossings. The resulting probabilities of failures were directly compared to those predicted based on expert judgement. The high ranked sites compared favorably with those evaluated by experts to exhibit elevated threats. This successful comparison provides a certain level of confidence in the proposed approaches.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".