Bibliographic record
Abstract
Enbridge Pipelines Inc. owns and operates the world’s longest hydrocarbon transmission system, which traverses across the varying geophysical landforms of Canada and the United States. These pipelines range in diameter from 12” to 48” and were constructed between 1950 and 2003. The wide range of pipe sizes, practices used for construction, and landforms traversed result in a very challenging Dent Management Program. Standards such as CSA Z662-99, ASME B31.4, and B31.8 provide a criterion for the selection of dents that require repair. Experience has shown that these standards do not identify all dents that have a possibility of failure due to leak or rupture. Enbridge initiated a project to study dents with BMT Fleet Technology of Kanata Ontario, this study determined that the dent geometry in addition to the depth to pipe diameter ratio affects the propensity that a dent will fail. Recent research and development by a group sponsored project lead by BMT Fleet technology on dent characterization has combined the pipeline’s cyclic pressure history with the shape of the dent to predict a time to failure. Enbridge combines these tools along with new insights from field excavations into its Dent Management Program. The Dent Management Program includes a series of prioritization’s to determine which sections of pipelines require detailed dent analysis. Typical prioritization criteria are rocky terrain, larger occurrence of third party damage, and history of numerous dents or failures. The detailed analysis utilizes the BMT Fleet “Dent Characterization Criteria” which was developed using their Finite Element Dent Assessment Model. This model considers the geometry of the dent, pipe material properties and historical pressure data to predict a time to failure for each dent. This time to failure prediction requires some additional engineering analysis depending on how close the parameters of the actual pipe are to what was validated with the model. This engineering analysis will determine which dents are selected for excavation and examination. This model has provided Enbridge with a tool to better manage its dent program, and this will be proposed as an option to improve the existing standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".