A Decoupled Buckling Failure Analysis of Buried Steel Pipeline Subjected to the Strike-Slip Fault
Bibliographic record
Abstract
Over the past few years, there has been an increased focus on offshore pipeline safety due to the development of offshore oil and gas resources. Both onshore and offshore pipelines may face significant geological hazards resulting from active faults. Pre-excavated soil can be used as backfill for trenches to prevent major pipeline deformations. Since these backfill materials have been heavily remolded, they are softer than the native soil. Therefore, the difference in shear strength between the backfill and native ground may have an effect on the interaction between the pipeline and the backfill. In this paper, the pipeline–backfill–trench interaction is investigated using a hybrid beam–spring model. The P-Y curves obtained from CEL analysis are incorporated into a 3D beam–spring model to analyze the pipeline’s response to lateral strike-slip faults. Additionally, the nonlinearity of pipeline materials is considered to study pipeline failure modes under strike-slip fault movements. A series of parametric studies were conducted to explore the effects of fault intersection angle, pipe diameter, buried depth of the pipe, and soil conditions on the failure modes of buckling pipelines. The developed method can be used to analyze and assess pipeline–backfill–trench interaction when subjected to strike-slip fault displacements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".