A Methodology to Maintain Pipeline Integrity at Water Crossings
Bibliographic record
Abstract
As the pipeline system of TransCanada Pipelines Ltd. (TransCanada) ages, cover at water crossings is continuously being adjusted to dynamic changes in weather patterns and local water crossing hydraulic characteristics. In an increased asset base of over 37 000 km of pipeline, this creates challenges to find and remediate crossings with high risks while maintaining the integrity of the whole system. A methodology has been developed to address the increasing demands of fiscal responsibility and pipeline integrity. The Scour Hazard Database Model (SHDM) provides the necessary tool to provide solutions to both of these challenges. The SHDM provides a stand alone prioritisation tool that is updateable and transparent. It can alert TransCanada to both immediate and potential pipeline exposures, in order that reactive and proactive solutions can be initiated. The SHDM contains descriptive pipeline information, local hydrologic data, channel hydraulic information, and scour hazard logic for over 2350 river and creek crossings throughout Canada. This information is used to produce a final rating value for comparing the potential for vertical and lateral pipeline exposures at each crossing. The vertical scour logic considers age of the crossing, modelled scour, natural degradation and any remedial work to determine the rating value. The lateral erosion logic uses channel form, location, lateral cover distances between the thalweg and pipeline, stream power, age of the crossing, and any remedial work to develop the lateral scour rating value. Furthermore, the exposed pipes are evaluated based on the potential failure mechanisms to determine failure probability. Included in the failure analysis are lateral stability, impact of debris, and fatigue. The failure probability and the consequence of the failure are used to rank the crossings and identify the requirement for maintenance activities.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".