Pipeline–soil–water interaction modelling for submarine landslide impact on suspended offshore pipelines
Bibliographic record
Abstract
The submarine landslide is one of the major geohazards in deep-water oil and gas developments. The impacts of glide blocks or out-runner blocks, which carry the geotechnical properties of the parent soil mass before the landslide, on pipelines normal to the direction of slide, are investigated in this study. A computationally efficient numerical modelling technique is developed using a computational fluid dynamics approach, incorporating a strain-rate and strain-softening dependent model for the undrained shear strength of clay sediment, to simulate the lateral penetration of a pipe in a clay block. The role of water in the cavity and channel formed behind the pipe during the lateral penetration on drag force is successfully simulated. Numerical simulations for varying depths of the pipe explain the change in soil failure mechanisms in which the channel behind the pipe and berm play a significant role, especially at shallow depths. As the cavity behind the pipe may not be completely filled with soil, the limitations of smooth/rough and bonded/unbonded interface conditions, as used typically in pipe–soil interaction analysis, are discussed. Based on a comprehensive parametric study, calibrated against centrifuge test results, a set of empirical equations is proposed to calculate drag force for practical applications. The effects of inertia on drag force are examined.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".