Satellite-Based Monitoring of Slope Movements on TransCanada’s Pipeline System
Bibliographic record
Abstract
TransCanada owns and operates over 38,000 km of pipeline throughout North America, which cross over 3,300 slopes and 1,200 watercourses. Ground movements on slopes at river crossings are an important pipeline hazard across Canada and especially within the Alberta system. These movements have led to several past pipeline ruptures and the development of a relatively extensive slope monitoring program. Historically, ground movement impacts are an industry-wide problem. The results of a 1998 study by the Gas Research Institute reported that external force damage from natural forces, including ground movement, was responsible for approximately 12 percent of all incidents reported on U.S. onshore pipelines between 1985 and 1994. Of all natural force incidents, ground movement accounted for approximately 29 percent of the total, on average. Furthermore, of all fires or explosions resulting from pipeline incidents, ground movements were reported responsible for about 5 percent of the total. In a similar study of Alberta pipeline failures and incidents between 1980 and 1997 (EUB, 1998), ground movement was the cause of 56 ruptures, or 3.5 percent of the total. Until recently, monitoring of the progress of slope movements was reactive and undertaken in a traditional fashion, using primarily slope inclinometers and/or ground surveys. Recently, however, TransCanada has adopted a proactive approach for the management of ground movements. Consistent with the management of other pipeline hazards, such as corrosion, ground movements are cast in a risk-based framework. The application of DInSAR technology, Differential Interferometry applied to satellite synthetic aperture radar (SAR) imagery, fits well within the proactive approach and has proven successful in measuring ground movements on ROW slopes to sub-centimetre accuracy. In 2000, a Pipeline Research Committee International (PRCI) study was carried out on a TransCanada Right of Way (RoW) that compared conventional slope indicator readings with DInSAR technology and proved the capability of the technology. TransCanada has begun to use DInSAR technology in this program of monitoring Alberta slopes. Typically, TransCanada monitors slope movements at 53 sites with frequency of readings between bi-annually and 4 times per year using conventional methods. Since 2001, 14 slopes on the TransCanada system have been instrumented using DInSAR methods and monitoring of movements using interferometric methods is continuing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".