Analytical Simulation and Field Measurements for a Wrinkle on the Norman Wells Pipeline
Bibliographic record
Abstract
In laboratory testing of full-sized pipe subjected to combined axial load, internal pressure and bending, the normal failure mode consists of local buckling on the side of the maximum compressive fiber. If loading is continued beyond the initiation of buckling, and if the pipe is pressurized internally, the pipe retains its integrity while it develops a wrinkle in which very large deformations occur. Buried pipelines should behave in the same way. The paper presents the analyses carried out to assist in the interpretation of Geopig field evidence that, in September of 1997, a wrinkle existed on Slope 92 on the Norman-Wells Pipeline. A finite element analysis of a shell model of a segment of the pipe confirmed that the wrinkle should exist. A subsequent pipe dig uncovered the wrinkle which had a configuration remarkably similar to those observed in the laboratory and to the form predicted by the analysis. Supplementary geotechnical investigations, a review of operational procedures on the line, and additional testing to determine strains within wrinkles, are presented in companion papers elsewhere in this conference. These combined papers provide a thorough documentation relative to this most interesting case history.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".