Bibliographic record
Abstract
AbstractPipeline designs such as on-bottom stability, lateral buckling and axial walking analyses require reliable predictions of the as-laid pipeline embedment. The as-laid embedment is usually significantly larger than that predicted by its submerged self-weight alone. This is mainly attributed to the effects of the stress concentration in the touchdown zone and soil remolding caused by the pipeline dynamic motions during the laying process. Quantification of these effects is often made difficult by the lack of field observations. This study presents a detailed back-analysis of the observed pipeline as-laid embedment in soft clayey soils in the shallow waters of Bengal Bay. Statistical analysis is performed to characterize the inherent variability of the observed embedment. The observed dynamic embedment factor is found to be between 1.08 and 1.96. The degradation of the soil resistance to the pipeline during the dynamic laying process is back-analyzed by a sophisticated method in which the reduction of the stress concentration with the increased embedment of the pipeline is well considered. The remolded soil resistance to the pipeline is found to be 0.48~0.74 of the intact soil in the dynamic laying process. And the degree of soil resistance degradation is found to be smaller when the pipeline static embedment is larger.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".