Determination of bearing capacity of shallow foundations without using superposition approximation
Bibliographic record
Abstract
The Terzaghi superposition assumption has been widely used to determine the bearing capacity of shallow footings. Although this assumption always errs on the safe side, a rigorous procedure to calculate the bearing capacity is still of engineering value. This paper presents such a procedure that is free from errors as a result of the superposition assumption. It demonstrates that the ultimate bearing capacity can be precisely expressed by the Terzaghi equation, except that the bearing capacity factor Nγis dependent upon the surcharge ratio. A recently developed numerical method, i.e., the critical slip field method, is used to calculate the modification coefficient for modifying Nγ. It is found that this modification coefficient increases with the surcharge ratio at small values of surcharge ratio and then remains constant for large values of surcharge ratio. However, the errors invoked by the superposition assumption do not exceed 10%. On the basis of numerical calculations, a simple closed-form expression of the modification coefficient is proposed that yields the theoretically rigorous ultimate bearing capacity. In the later part of the paper, errors in bearing capacity calculations owing to the use of conventional procedures are analyzed. It is concluded that the continued use of conventional procedures is justified, but the inherent errors should not be neglected in assessing the performance of shallow foundations.Key words: shallow foundation, strip footing, ultimate bearing capacity, critical slip field.
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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) | 0.000 | 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 | 0.000 | 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".