Technical and Operational Guidelines When Using Strain Gauges to Monitor Pipelines in Slow Moving Landslides
Bibliographic record
Abstract
Vibrating wire (VW) strain gauges have been used by the pipeline industry for over 50 years as part of landslide hazard management programs. This paper provides technical and operational guidelines for the use of these strain gauges based on 20 years of experience managing active but slow moving landslides. Guidelines are provided for the use of strain gauges during 1) routine monitoring 2) cut outs and 3) strain relief. Examples of expected strain gauge responses are provided along with technical considerations for interpreting data. Given the relatively small size of the gauges in relation to the length of pipeline within most landslides, techniques are provided to best locate the gauges including the use of 1) visual/on-site geotechnical assessments, 2) geotechnical monitoring technologies and 3) smart pigging technologies (caliper, IMU and axial strain technologies). Limitations, reliability, and alternatives to VW gauges are also discussed.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".