A finite element approach for determining the full load–displacement relationship of axially loaded shallow screw anchors, incorporating installation effects
Bibliographic record
Abstract
Screw anchors have been recognised as an innovative solution to support offshore jacket structures and floating systems, due to their low-noise installation and potential enhanced uplift capacity. Results published in the literature have shown that for both fixed and floating applications, the tension capacity is critical for design, but may be poorly predicted by current empirical design approaches. These methods also do not capture the load–displacement behaviour, which is critical for quantifying performance under working loads. In this paper, a finite element methodology is developed to predict the full tensile load–displacement response of shallow screw anchors installed in sand for practical use, incorporating the effects of a pitch-matched installation. The methodology is based on a two-step process. An initial simulation, based on wished-in-place conditions, enables the identification of the failure mechanism as well as the shear strain distribution at failure. A second simulation refines the anchor capacity using soil–soil interface finite elements along the failure surface identified previously and also models installation through successive loading–unloading of the screw anchor at different embedment depths. The methodology is validated against previously published centrifuge test results. This simplified numerical approach has been derived to approximate the results in a single step.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".