How Many Pipelines in North America Have Failed by Fatigue and Why?
Bibliographic record
Abstract
Pipelines are aging: more than half of all pipelines in Europe and the United States are over 40 years old. Historically, only a small number of pipeline failures have been attributed to fatigue; however, as pipelines age, this might change. Indeed, two of the most serious pipelines failures in recent years in the United States were partly attributed to fatigue. The issue with fatigue is not so much how it should be addressed, but if or when, and where, it will become more of a problem. Historical failure data provides a valuable insight into the number and cause of failures that have been attributed to fatigue, and an indication of what might happen in the future. Historical failure data for onshore gas and liquid pipelines in the United States of America and Canada has been reviewed in order to estimate the number and cause of failures that can be attributed to fatigue; specifically, the OPS 30-day Incident Reports, the listing of pipeline rupture events compiled by the National Energy Board, and the findings of failure investigations conducted by the National Transportation Safety Board (NTSB) and the Transportation Safety Board of Canada (TSB). Failures that can (at least partly) be attributed to fatigue are not readily identifiable in the historical data, because fatigue is not listed as a secondary cause (as it is, strictly, only a growth mechanism). The narrative descriptions in historical data sets, as in the OPS 30-day Incident Reports, and the detail in the Pipeline Investigation Reports or Accident Briefs published by the NTSB, and the Pipeline Investigation Reports published by the TSB are essential for identifying the relevant failures and their causes. Failures in pipelines that can be attributed to fatigue are relatively rare, but fatigue failures have been reported in both onshore gas and liquid pipelines in both the United States and Canada, mostly originating from pre-existing mechanical damage or manufacturing defects. Corrosion-fatigue has been identified as a contributing factor in a minority of the failures. The number of failures in liquid pipelines is (as would be expected) higher than that in gas pipelines. The number of failures in onshore liquid pipelines in the United States that can be attributed to fatigue has increased, with over half of such failures having occurred in the last ten years. The increase is statistically significant. There has also been an increase, albeit smaller and not statistically significant, in the number in onshore gas pipelines. The increase in the number of failures is consistent with an ageing system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".