Ground Movement Monitoring of Unstable Pipeline Corridors With Fiber Optic Slope Indicators
Bibliographic record
Abstract
Spectra Energy Transmission (SET) owns and operates approximately 6,000 kms of raw and sweet natural gas transmission pipelines in northeastern British Columbia and northwestern Alberta. This geographic area is very susceptible to landslides and unstable land mass due principally to the local geological regime. These slope instabilities present long term operational challenges to pipeline companies. Geotechnical pipeline failures are not uncommon and pipeline operators spend significant portions of their operational budgets on geotechnical issues. SET has developed a geotechnical integrity program to take a proactive approach to these geotechnical issues. Ground movement monitoring is a significant component of this integrity program and provides physical data that becomes the backbone of remedial works. SET currently utilizes traditional slope indicators, surface survey monitoring, differential GPS, LiDar and InSar technologies to obtain this ground movement data. As an element of the geotechnical integrity program, SET utilizes fiber optic sensors to monitor the pipeline’s reaction to ground movement. After the initial installation of these fiber optic sensors, it was apparent that they could be bonded to almost any structural member. Potential to use the fiber optic sensors to extend the life of a traditional slope indicator was discussed with the sensor manufacturer and six joints of slope indicator casing were fitted with fiber optic sensors. These instrumented joints were then installed across known slide surfaces at various existing monitoring locations. Periodic data collection of both the slope indicator and the fiber optic sensors allowed for ground movement correlation up to the shearing of the slope indicator. It is anticipated that with proper installation and further design improvements from the manufacturer that the fiber optic instrumented slope inclinometers will facilitate ground movement monitoring beyond the life of the traditional slope indicator. This paper discusses the results of the initial trial, what was successful, what lessons were learned, and which pipeline scenarios would benefit from this technology and potential methodologies to monitor ground movement and pipeline bending concurrently.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".