Effect of footing width on<i>N</i><sub><i>γ</i></sub>
Bibliographic record
Abstract
By incorporating the dependency of soil friction angle (ϕ) on mean principal stress (σ m ), the effect of footing width (B) on bearing capacity factor (N γ ) is examined for a rough strip footing. The analysis is performed by means of a numerical lower bound limit analysis in conjunction with triangular finite elements and linear programming. To account for the variation of ϕ with σ m , a solution is obtained by using an iterative procedure. Two well defined ϕ–σ m curves from the literature, associated with Hoston and Toyoura sands that correspond to relative densities of 18% and 74.5%, respectively, are used. The magnitude of N γ is computed for different footing widths. It is noted that for B greater than about 0.2 m, the magnitude of N γ varies almost linearly with B on a log–log scale. For different footing widths, a good comparison is seen between the obtained rigorous solution and that obtained by using a constant value of ϕ, which corresponds to the equivalent mean normal stress level as defined by De Beer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".