Incorporating set-up into reliability-based design of driven piles in clay
Bibliographic record
Abstract
A statistical database is developed to describe the increase in pile axial capacity with time, known as set-up, when piles are driven into clay. Based on the collected pile testing data, pile set-up is significant and continues to develop for a long time after pile installation. The statistical database shows that normal distribution can be used to properly describe the probabilistic characteristics of predicted set-up capacity by the Skov and Denver equation. The main objective of this paper is to incorporate the set-up effect into a reliability-based load and resistance factor design (LRFD) of driven piles. The statistical parameters for set-up effect combined with the previously documented statistics of load and resistance can be systematically accounted for in the framework of reliability-based analysis using the first-order reliability method (FORM). Separate resistance factors are obtained to account for different degrees of uncertainties associated with measured short-term capacity and predicted set-up capacity at various reliability levels. The incorporation of set-up effect in LRFD can improve the prediction of design capacity of driven piles. Thus, pile length or numbers of pile could be reduced and economical design of driven piles could be achieved.Key words: driven piles, set-up, reliability, load and resistance factor design (LRFD), first-order reliability method (FORM).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".