Reliability of bored pile foundations considering bias in failure criteria
Bibliographic record
Abstract
The failure of a pile is always defined by a certain failure criterion. Several different failure criteria are commonly used, and the pile capacity values associated with each of these failure criteria can be considerably different. For the sake of international harmonization, it is necessary to calibrate the reliability levels associated with various failure criteria and factors for loads and resistances. This paper aims to evaluate the effects of failure criteria and factors for loads and resistances on the reliability of single bored piles. The bias arising from failure criteria is described by a bias factor, which can easily be accommodated in a reliability analysis. A comprehensive database of static load tests of bored piles is utilized to evaluate the bias associated with several failure criteria. Five limit-state design codes for piles are investigated to illustrate the effect of the bias from failure criteria, the effect of factors for loads and resistances, and their combined effect. The results indicate that the bias from failure criteria has a significant influence on the reliability of piles. Similarly, the use of different factors for loads and resistances in various design codes can also cause considerable differences in the calculated reliability. As a result of these effects, the actual reliability levels of any two design codes, assuming the same nominal target reliability index, can differ considerably.Key words: bored piles, pile capacity, failure criterion, reliability analysis, load factors, resistance factors.
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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 | 0.002 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".