Capacity of dynamically installed anchors as assessed through field testing and three-dimensional large-deformation finite element analyses
Bibliographic record
Abstract
The capacity of dynamically installed anchors in soft normally consolidated clay was examined experimentally through a series of field tests on a 1:20 reduced-scale anchor. The anchors were installed through free fall in water, achieving tip embedment of 1.5–2.6 times the anchor length, before being loaded under undrained conditions at various load inclinations. Vertical anchor capacities were between 2.4 and 4.1 times the anchor dry weight and were satisfactorily predicted using the American Petroleum Institute approach for driven piles. Anchor capacity under inclined loading increased as the load inclination approached horizontal; the field data indicated this increase to be up to 30% for the minimum achievable inclination of about 20° to the horizontal. Corresponding large-deformation finite element analyses showed a similar response, with the maximum capacity occurring at a load inclination between 30° and 45° to the horizontal. The finite element results demonstrate that, for the anchor geometry considered, an inclined load at the anchor padeye could be decomposed into ultimate vertical and moment loading at the anchor centroid. The establishment of a vertical and moment loading yield envelope for the geometry investigated forms the basis of a simple design procedure presented in the paper.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".