Optimization fo rock berms for pipeline stabilization subject to intense hydrodynamic forcing
Bibliographic record
Abstract
This article describes a comprehensive study in which 2D and 3D physical modelling at 1:40 scale was used to optimize the design and validate the performance of dynamically stable rock berms to be used for stabilizing several large pipelines traversing water depths from 5m to 65m and potentially exposed to large waves and strong currents generated by intense tropical cyclones. For added realism, all of the model rock berms were constructed using a scaled simulation of rock installation by fall pipe vessel to be used in the field. Special attention was also given to simulating the self-stability of the model pipeline segments, including special end constraints designed to mimic the behaviour of a continuous pipeline. A large data set concerning the behaviour of dynamically reshaping rock berms in a range of water depths under intense hydrodynamic forcing due to three-dimensional waves and currents was produced and used to develop efficient and costeffective rock berm designs for all depth zones.
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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) | 0.000 | 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 | 0.001 | 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".