Effects of Post-Peak Softening Behavior of Dense Sand on Lateral and Upward Displacement of Buried Pipelines
Bibliographic record
Abstract
Buried pipelines are extensively used in onshore and offshore for transportation of hydrocarbons. The response of pipeline due to lateral and upward relative displacements is one of the major concerns in pipeline design. Both physical modeling and numerical analyses have been performed in the past to understand pipeline-soil interaction mechanisms. The numerical analyses are generally performed using finite element (FE) modeling techniques. For the pipelines buried in sand, a large number of analyses available in the literature have been performed using the Mohr-Coulomb model assigning constant values of angle of internal friction (ϕ′) and dilation (ψ). However, dense sand shows post-peak softening behavior and the behavior of sand also depends on mode of shearing, such as triaxial (TX), direct shear (DS) or direct simple shear (DSS) conditions. In the present study, FE analysis of buried pipelines in dense sand is presented. The first set of analyses are performed using the built-in Mohr-Coulomb model in Abaqus FE software with constant angles of internal friction and dilation, as typically used in previous FE analysis of pipeline-soil interaction. The second set of analyses are performed using a modified Mohr-Coulomb model where pre-peak hardening, post-peak softening, density and confining pressure dependent friction and dilation angles are considered. The FE analyses are performed using the Arbitrary Lagrangian-Eulerian (ALE) approach available in Abaqus/Explicit FE software. The modified Mohr-Coulomb model is implemented in Abaqus FE software using a user defined subroutine. Shear band formation due to strain localization and failure patterns for both lateral and upward pipeline-soil interactions are discussed from the simulations with MC and MMC models. FE results show that the MMC model can simulate the load-displacement behavior and failure pattern better than the simulations with the MC model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".