Remaining Local Buckling Resistance of Corroded Pipelines
Bibliographic record
Abstract
This paper describes a multi-year PRCI research program that investigated the local buckling (or wrinkling) of onshore pipelines with metal-loss corrosion. The dependence of local buckling resistance on wall thickness suggests that metal-loss defects will considerably reduce such resistance. Due to the lack of experimental data, overly conservative assumptions such as a uniform wall thickness reduction over the entire pipe circumference based on the defect depth have been used in practice. The objective of this research work was to develop local buckling criteria for pipelines with corrosion defects. The work related to local buckling was carried out in three phases by C-FER and the University of Alberta. The first phase included a comprehensive finite element analysis to evaluate the influence of various corrosion defect features and to rank key parameters. Based on the outcome of Phase 1 work, a test matrix was developed and ten full-scale tests were carried out in Phase 2 to collect data for model verification. In Phase 3, over 150 parametric cases were analyzed using finite element models to develop assessment criteria for maximum moment and compressive strain limit. Each criterion includes a set of partial safety factors that were calibrated to meet target reliabilities selected based on recent research related to pipeline code development. The proposed criteria were applied to in-service pipeline examples with general corrosion features to estimate the remaining load-carrying capacity and to assess the conservatism of current practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".