Ultimate limit state reliability-based design of augered cast-in-place piles considering lower-bound capacities
Bibliographic record
Abstract
The use of augered cast-in-place (ACIP) piles for transportation infrastructure requires an appropriate reliability-based design (RBD) procedure. In an effort to improve the accuracy of an existing design model and calibrate appropriate resistance factors, this study presents a significantly revised RBD methodology for estimating the shaft and toe bearing capacity of ACIP piles using a large database consisting of static loading tests in predominately granular soils. The proposed design models are unbiased, as opposed to those currently recommended. Based on the reasonable assumption that a finite lower-bound resistance limit exists, lower-bound design lines are developed for shaft and toe bearing resistance by applying a constant ratio to the proposed design models. Resistance factors are calibrated at the strength or ultimate limit state (ULS) for ACIP piles loaded in compression and tension for two commonly used target probabilities of failure with and without lower-bound limits. For piles loaded in compression, separate resistance factors are calibrated for the proposed shaft and toe bearing resistance models. The inclusion of a lower-bound limit for piles loaded in tension results in a 24%–50% increase in the calibrated resistance factor. For piles loaded in compression, the application of a lower-bound limit results in a 20%–150% increase in the calibrated resistance factor, and represents a significant increase in useable pile capacity. Although the impact of a lower-bound limit on resistance factor calibration is directly dependent on the degree of uncertainty in the distribution of resistance, this effect is outweighed by the type of distribution selected (i.e., normal, lognormal) at more stringent target probabilities of failure due to differences in distribution shape at the location of the lower-bound limit. A companion paper explores the use of the revised ULS model in a reliability-based serviceability limit state design framework.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".