Bearing capacity factors for a conical footing using lower- and upper-bound finite elements limit analysis
Bibliographic record
Abstract
Bearing capacity factors, Nc, Nq, and Nγ, for a conical footing are determined by using the lower and upper bound axisymmetric formulation of the limit analysis in combination with finite elements and optimization. These factors are obtained in a bound form for a wide range of the values of cone apex angle (β) and [Formula: see text] with δ = 0, 0.5[Formula: see text], and [Formula: see text]. The bearing capacity factors for a perfectly rough (δ = [Formula: see text]) conical footing generally increase with a decrease in β. On the contrary, for δ = 0°, the factors Nc and Nq reduce gradually with a decrease in β. For δ = 0°, the factor Nγ for [Formula: see text] ≥ 35° becomes a minimum for β ≈ 90°. For δ = 0°, Nγ for [Formula: see text] ≤ 30°, as in the case of δ = [Formula: see text], generally reduces with an increase in β. The failure and nodal velocity patterns are also examined. The results compare well with different numerical solutions and centrifuge tests’ data available from the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".