Influence of Low Confining Pressure in Modeling of Lateral Pipeline/Soil Interaction in Dense Sand
Bibliographic record
Abstract
Buried pipelines are extensively used for transporting water and hydrocarbons. Geohazards and associated ground movements represent a significant threat to pipeline integrity that may result in pipeline damage and potential failure. Safe, economic and reliable operation of pipeline transportation systems is the primary goal of the pipeline operators and regulatory agencies. The pipes are often buried at a shallow depth and therefore the behaviour of soil at low stress level need to be considered for proper modeling of the response of pipelines. In this study, finite element (FE) modeling of pipeline/soil interaction is presented, where the stress-stain behaviour of soil at low stress level is implemented. At first, triaxial test results are simulated to validate the proposed model and numerical techniques. Pipeline/soil interaction in plane strain condition is then simulated for lateral loading. The Arbitrary Lagrangian-Eulerian (ALE) method available in Abaqus/Explicit is used for FE modeling. One of the main advantages of this method is that it can simulate large deformation behaviour. The variation of non-dimensional lateral force with non-dimensional displacement is examined for different depth of embedment of pipeline and soil conditions. Finally, shear band formation in soil due to lateral movement of the pipe is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".