Determining the bearing capacity factor due to nonlinear matric suction distribution in the soil
Bibliographic record
Abstract
Bearing capacity is often calculated in dry or saturated conditions, leading to overconservative designs, for a wide range of climates in the world. Extensive researches show that bearing capacity is significantly affected by the soil matric suction. However, in most of the presented models, uniform (and sometimes linear) suction distributions are taken into account for computing the bearing capacity. Also, there is no exact solution in the residual zone of unsaturation. In the present study, a simple method is proposed to predict the bearing capacity of footings placed on unsaturated soil, using the limit equilibrium concept. Linear and uniform variations of matric suction are considered in computations, as well as the nonlinear suction distribution. The framework of the proposed model is analogous to Terzaghi’s equation, and a novel factor is developed, during calculations, as the suction bearing capacity factor. In the case of full saturation, the proposed model is simplified to the Terzaghi’s equation. Estimated results are compared with the experimental and theoretical data available in the literature. Predicted values are in a good agreement with the measured data in the transition zone and residual zone of unsaturation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".