Influence of a nonlinear failure criterion on the bearing capacity of a strip footing resting on rock mass using a lower bound approach
Bibliographic record
Abstract
The strength envelope of almost all geomaterials is nonlinear when one considers a wide range of stresses. Therefore, a nonlinear failure criterion needs to be used in stability analysis whenever the effects of nonlinearity are too significant to be neglected. This paper presents a lower bound solution to the bearing capacity calculation of a strip footing resting on a homogenous weightless rock mass using the nonlinear HoekBrown (HB) failure criterion. Two types of admissible stress fields are used to develop solutions. The first stress field has three stress legs. The second stress field has a spiral-like shape with n stress legs, where n may vary from 9 to 18 000 (even to infinity). Using two admissible stress fields, equations are derived and used for the calculation of lower bound bearing capacity values of a strip footing on rock mass. The influences of the admissible stress leg number n and the material parameter s in the nonlinear HB failure criterion are investigated. It is found that the lower bound bearing capacity calculated using the spiral-like shape admissible stress field approaches to the true optimum value as the stress leg number n increases, and the nonlinear material parameter s has a strong influence on the bearing capacity of the footing.Key words: HB failure criterion, bearing capacity, stress leg, lower bound theorem, rock.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".