Incorporating setup into load and resistance factor design of driven piles in sand
Bibliographic record
Abstract
A comprehensive database is developed for the setup for piles driven into sand. Based on the compiled pile-testing data, pile setup is significant and continues to develop for a long time after pile installation. The statistical analysis shows that a logarithm-normal distribution can be used to describe the probabilistic characteristics of the predicted setup capacity using the Skov and Denver equation. The main objective of this paper is to incorporate the setup effect into a reliability-based load and resistance factor design (LRFD) of driven piles in sand. The first-order reliability method (FORM) is used to derive separate resistance factors that would account for different degrees of uncertainties associated with measured short-term capacity and predicted setup capacity. The incorporation of setup effects in the LRFD helps improve the prediction of total capacity of driven piles, resulting in more economical design. A practical design procedure within the LRFD framework to incorporate the pile setup effects is outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".