Full-Scale Physical Modeling of Axial Soil-Pipe Interaction in Organic Soils
Bibliographic record
Abstract
Abstract Organic soils (i.e., muskeg, peat deposits) cover 18% of the Canadian landscape and many energy pipelines cross these soil terrains over large distances. Thermal changes due to operational and environmental reasons pose a significant threat to the structural integrity and safety of pipeline systems in these soils. Engineering design of pipelines in muskeg terrains involves many challenges, mainly due to the lack of understanding of the mechanical behavior of organic soils. Such knowledge gaps have caused an absence of well-adapted soil-pipe interaction (SPI) assessment methodologies for pipeline design in organic soils, unlike the methods readily available for pipes buried in mineral sandy/clayey soils (e.g., PRCI guidelines [1]). In view of the above, a detailed research program is undertaken to study the soil-pipe interaction in organic soils. As a part of this program, a series of full-scale axial pipe displacement tests was conducted using a sand blasted 114 mm diameter steel pipe buried in organic soil simulating different H/D ratios, directly yielding axial p-y curves (i.e., soil springs). The “soil springs” representing muskeg are compared with those computed using equations given in PRCI guidelines [1] for pipes buried in clayey soils. Based on this, adjustments to these clay-based equations are proposed for generating axial soil springs for pipes buried in organic soils.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".