Bearing capacity of spatially random soil: the undrained clay Prandtl problem revisited
Bibliographic record
Abstract
By merging elasto-plastic finite element analysis with random field theory, an investigation has been performed into the bearing capacity of undrained clays with spatially varying shear strength. The object of the investigation is to determine the extent to which variance and spatial correlation of the soil's undrained shear strength impact on the statistics of the bearing capacity. Throughout this study, bearing capacity results are expressed in terms of the bearing capacity factor, N c , in relation to the mean undrained strength. For low coefficients of variation of shear strength, the expected value of the bearing capacity factor tends to the Prandtl solution of N c = 5·14. For higher values of the coefficient of variation, however, the expected value of the bearing capacity factor falls quite steeply. The spatial correlation length is also shown to be an important parameter that cannot be ignored. The results of Monte Carlo simulations on this non-linear problem are presented in the form of histograms, which enable the interpretation to be expressed in a probabilistic context. Results obtained in this study help to explain the well-known requirement that bearing capacity calculations require relatively high factors of safety compared with other branches of geotechnical design.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".