Predicting the resistance profile of a spudcan penetrating sand overlying clay
Bibliographic record
Abstract
Assessment of the risk of punch-through failure of spudcan foundations on sand overlying clay requires prediction of the full penetration resistance profile, from touchdown and through punch-through to equilibrium of the vertical resistance at depth in the underlying clay layer. This study uses the Coupled Eulerian–Lagrangian approach, a large deformation finite element analysis method, to model the complete penetration resistance profile of a spudcan on sand overlying clay. The sand is modeled using the Mohr–Coulomb model, while the clay is modeled using a modified Tresca model to account for strain softening. The numerical method is then used to simulate a series of spudcan penetration tests, performed in a geotechnical centrifuge, on medium dense sand overlying clay. The punch-through behavior observed in the experiments is replicated, and the penetration resistance profiles from numerical analyses are generally a reasonable match to the experimental measurements. The influences of the sand layer height to foundation diameter ratio, sand–clay interface shear strength, and strength gradient in clay on the penetration resistance profiles are explored in a complementary parametric study. The penetration resistance in the underlying clay layer is well predicted using a simple linear expression for the bearing capacity factor for the spudcan and underlying sand plug. This expression is combined with an existing failure stress dependent model for predicting peak resistance to form a simplified method for prediction of the full penetration resistance profile. This new method provides estimates of the vertical penetration that the spudcan will run during the punch-through event. It is validated against both medium dense and dense sand centrifuge tests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".