Bearing capacity of strip footings on sand over clay
Bibliographic record
Abstract
Estimation of the bearing capacity of shallow foundations on layered soil profiles, such as a sand layer of finite thickness over clay, is mainly based on empirical models resulting from the interpretation of experimental test results. While it is generally accepted that such models may be applicable to soil properties and footing geometries outside the range tested experimentally, they offer limited insights on how the assumed failure mechanism affects their range of application. In particular, the contribution of the sand layer to the overall capacity is accounted for via simple considerations, which are valid only for a specific range of problem parameters. This paper addresses the estimation of the undrained bearing capacity of a rigid strip footing resting on the surface of a sand layer of finite thickness overlying clay, using finite element limit analysis (FELA). The rigorous upper and lower bound theorems of plasticity are employed to bracket the true bearing capacity of the footing, and identify the geometry of possible failure mechanisms. Insights gained from FELA simulations are used to develop a new simple bearing capacity model, which captures the variation in shear resistance from the sand layer with the dimensionless undrained strength of the clay layer. The proposed model provides results that are in close agreement with published experimental studies, and allows treating simple problems, such as the design of working platforms, without having to resort to numerical simulations.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".