Geometrical Nonlinearities on the Bearing Capacity in Clay: A Validation Data Set for Numerical Tools
Bibliographic record
Abstract
A series of plate loading tests on clay has been conducted in the centrifuge. The aim of the tests is to create a data set, which is freely downloadable, to validate numerical tools that account for geometrical nonlinearities. The tests include two sources of geometrical non-linearities. The first source is the reducing clay layer thickness below the plate, which causes an increase in resistance. The second source is the backflow of the clay around the tip of the plate. The backflow has a reducing effect on the plate resistance. This paper outlines four tests: two involving a wide plate and two with a small plate. Each plate geometry is investigated under both smooth and rough side model boundaries. An material point method (MPM) schematization is used for numerical analysis. The schematization and parameter selection are initially validated by comparing the MPM results against CPTu data in each test. The numerical analysis examines the impact of a finite layer thickness by analyzing various layer thicknesses. Furthermore, the analysis shows the influence of the backflow on the plate resistance by analyzing different ratios of shaft to plate width. In this study, the pore pressures below the plate and vertical and horizontal displacement fields are considered in addition to the load displacement curves. The MPM simulations are in good agreement with the centrifuge data.
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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) | 0.000 | 0.001 |
| 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".