Stress Analysis of Surface Pipelines Located in Regions of Differential Ground Movements
Bibliographic record
Abstract
Buried pipelines running through areas of large differential ground movements, such as active landslides, can be subject to severe overstress conditions. Moving these pipelines to the surface (i.e. surfacing) and resting them on timber skids reduces the impact of ground movements because slippage can occur between the pipe and the skids. This approach has been used successfully on several pipelines. Surfacing was recently considered for a 4.5 km section of a buried pipeline situated in northern British Columbia. The surfaced pipeline is supported by timber skids every 2 m and winds through mountainous terrain containing twenty different movement zones in which differential displacements range from 0 m to 3 m. Along the route, the direction of the ground movement varied with the terrain. The alignment also crossed a creek with a 45 m clear span. This paper describes the methodology used in completing the stress analysis for the surfaced pipeline. To capture various load effects, an existing construction stage analysis program was modified to facilitate the modelling of the surface pipeline under the various load paths. The evolution of the displacement and stress state of the pipeline was determined and tracked for several different load paths. For each state, a single plot that summarized the ground movement, slip distances, skid reactions and pipe stresses was produced. The results from this analysis are used to discuss the response of surface pipelines under thermal and ground movement loads.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".