Slope Movement Inspection Using Axial Strain Data Across Multiple Lines and Repeat Inspections
Bibliographic record
Abstract
Abstract Axial strain inspection using the AXISS™ is an established tool in the pipeline operator’s toolbox to assess pipeline geotechnical threats and other strain related events. Consequently, there is a large database of axial strain data for several different pipelines operating in different environments and from multiple inspections at the same geographical locations. The Cheecham slope, located south east of Fort McMurray, Alberta, is a known geohazard site crossed by six individual pipelines. The lines were constructed between 1999 and 2013 and have a size range of 10” to 36”. Five out of the six lines, 12” to 36”, have been inspected using the axial strain tool. The pipelines inspected cover a range of characteristics including, different vintages, pipe diameters and positions in the ROW. These differences, and the ILI runs provide an insight into the effect of a landslide event on the strain response of these pipelines. Axial strain measurement of the multiple pipelines in the Cheecham slope’s ROW allows: i) a direct comparison between lines ii) evaluation of the strain profile across the slope iii) assessment of the magnitude of the axial strain in terms of pipe characteristics e.g. pipe vintage and mechanical properties. More importantly, the axial strain data may provide an additional tool to assess the effectiveness of strain mitigation steps carried out over the years. An increase in the frequency of axial strain ILI runs resulted in additional data being available and more importantly data from run to run inspections spread over months or sometime years. A single run captures the strain at the time of inspection but run to run inspections provide an additional comparative tool to evaluate and monitor pipeline movement. Two out of the five lines inspected have run to run axial strain data. This paper takes the Cheecham slope as a case study to discuss the benefits of run comparison of ILI axial strain data either by comparing strain values of repeated runs for a single line or by the cross comparison of strain responses of different lines in the same ROW. The paper aims to demonstrate how run to run analysis of ILI axial strain data can be implemented as part of geohazard risk management program to asses strain risk profiles of these locations and to assess the effectiveness of strain mitigation programs previously undertaken by operators.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".