Generalized framework to predict undrained uplift capacity of buried offshore pipelines
Bibliographic record
Abstract
Estimation of undrained uplift capacity is essential for the determination of optimal burial depth of buried offshore pipelines. However, a generalized prediction model that incorporates various factors influencing this capacity is scarce in the literature. In this paper, results from a series of small-strain finite element analyses are presented that explore the effects of pipe embedment, pipe–soil interface roughness, interface tensile capacity, soil shear strength, and unit weight on pipe uplift response. From the study, a simple method to predict the undrained upheaval resistance of buried pipelines for any practical range of pipeline and soil parameters is proposed. Factors associated with transition in failure mechanism with embedment are also examined. The numerical model is validated by comparing the results with available analytical and experimental data. Large-deformation finite element analyses have also been performed independently for a few cases to justify the applicability of small-strain methods in modelling pipe upheaval. Accuracy of the model for generalized shear strength profile is then examined by considering practical values of parameters over broad ranges. The proposed methodology gives results with maximum error less than 8% for all ranges of parameters and hence can be adopted in design practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".