Serviceability limit state reliability-based design of augered cast-in-place piles in granular soils
Bibliographic record
Abstract
This study proposes a reliability-based design procedure to evaluate the allowable load for augered cast-in-place (ACIP) piles installed in predominately granular soils based on a prescribed level of reliability at the serviceability limit state. The ultimate limit state (ULS) ACIP pile–specific design model proposed in the companion paper is incorporated into a bivariate hyperbolic load–displacement model capable of describing the variability in the load–displacement relationship for a wide range of pile displacements. Following the approach outlined in the companion paper, distributions with truncated lower-bound capacities are incorporated into the reliability analyses. A lumped load-and-resistance factor is calibrated using a suitable performance function and Monte Carlo simulations. The average and conservative 95% lower-bound prediction intervals for the calibrated load-and-resistance factor resulting from the simulations are provided. Although unaccounted for in past studies, the slenderness ratio is shown to have significant influence on foundation reliability. Because of the low uncertainty in the proposed ULS pile capacity prediction model, the use of a truncated distribution has moderate influence on foundation reliability.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".