A macroelement model for predicting load–displacement reponses of suction anchors under combined horizontal–vertical–torsional loading in clay
Bibliographic record
Abstract
This paper presents a plastic-hardening, nonassociated macroelement model for predicting the load–displacement responses and padeye trajectories during the monotonic pullout process of suction anchors under horizontal–vertical–torsional loading ( H–V–T) in clay. The closed-form formula of the three-dimensional H–V–T failure surface is established through comprehensive finite-element analyses, extending from the conventional two-dimensional horizontal–vertical failure envelope and considering the influence of anchor aspect ratio and reduction factor of interface shear strength. Subsequently, appropriate forms of loading surface and hardening rule are selected based on this failure surface. A nonassociated flow rule is adopted with a modified plastic potential that accurately captures the padeye kinematic trajectories. Only one test is required for determining and calibrating model parameters, along with recommended loading conditions provided. Finally, rigorous validation against numerical tests conducted in this study, as well as physical model tests and numerical simulations from other studies, demonstrates satisfactory agreement between the model predictions and experimental results. The proposed macroelement model offers researchers and engineers a straightforward and effective tool, particularly useful in scenarios involving suction anchors subjected to torsional load.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| 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".