Load and resistance factor design of shallow foundations against bearing failure
Bibliographic record
Abstract
Shallow foundation designs are typically governed either by settlement, a serviceability limit state, or by bearing capacity, an ultimate limit state. While geotechnical engineers have been designing against these limit states for over half a century, it is only recently that they have begun to migrate towards reliability-based designs. At the moment, reliability-based design codes are generally derived through calibration with traditional working stress designs. To take advantage of the full potential of reliability-based design the profession must go beyond calibration and take geotechnical uncertainties into account in a rational fashion. This paper proposes a load and resistance factor design (LRFD) approach for the bearing capacity design of a strip footing, using load factors as specified by structural codes. The resistance factors required to achieve an acceptable failure probability are estimated as a function of the spatial variability of the soil and by the level of “understanding” of the soil properties in the vicinity of the foundation. The analytical results, validated by simulation, are primarily intended to aid in the development of the next generation of reliability-based geotechnical design codes, but can also be used to assess the reliability of current designs.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".