Evaluation of lateral interpretation criteria for rigid drilled shafts
Bibliographic record
Abstract
Representative criteria are examined to evaluate the “interpreted failure load” or “capacity” of rigid drilled shaft foundations under lateral loading. Field lateral load test data are used for this analysis, consisting of both drained and undrained databases. It was found that a hyperbola describes the load–displacement data well and that the normalized undrained curve is stiffer, higher, and more sharply curving than the drained curve. The initial elastic region ends at approximately 1%B (where B is the shaft diameter),which represents serviceability limit state (SLS) conditions. The final region begins at about 4%–5%B, which represents ultimate limit state (ULS) conditions. Also, the QLmethod is most appropriate for interpreting the “failure load” because it is the only method that incorporates actual soil-shaft failure mechanisms as part of the interpretation, is the least variable, and has the lowest coefficient of variation (COV). Further detailed recommendations are given for assessing the load test data.
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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 | 0.015 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".