Towards a simple and reliable method for calculating uplift capacity of plate anchors in sand
Bibliographic record
Abstract
This paper investigates the uplift capacity of horizontal plate anchors in sand through finite element analyses and centrifuge experiments. The finite element simulations adopt a sophisticated bounding surface plasticity model that accounts for stress- and density-dependent behaviour, as well as loading- and fabric-related anisotropic effects in sands. Failure mechanisms at peak anchor capacity show that failure occurs progressively, with a marked decrease in mobilised friction angle within the shear bands close to the anchor edge. Numerical simulations of a large set of centrifuge experiments on rectangular, strip, and circular plates at different relative densities and stress levels are in good agreement for dense conditions, but perform more poorly for loose conditions due mainly to the open cone yield surface in the bounding surface model. Equivalent comparisons with current limit equilibrium methods highlight the challenges in direct application of element-level strength equations. Finally, the paper proposes a modified limit equilibrium solution based on a “rigid-block” failure mechanism extending to the soil surface, but with anchor factors that encompass the results from the finite element simulations. The modified solution provides a higher level of agreement with results from a large database of plate and pipeline test data than existing limit equilibrium methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".