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Record W2584036253 · doi:10.1115/ipc2016-64450

How Many Pipelines in North America Have Failed by Fatigue and Why?

2016· article· en· W2584036253 on OpenAlexaboutno aff
Andrew Cosham, Phil Hopkins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportPipeline (software)Forensic engineeringListing (finance)EngineeringBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.011
GPT teacher head0.207
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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