Design method reliability assessment from an extended database of axial load tests on piles driven in sand
Bibliographic record
Abstract
The accurate prediction of axial capacity remains a challenging task for piles driven in sands. Rigorous database studies have become key tools for assessing the efficacy of design methods. This paper employs the 117 high-quality entries in the recently developed Zhejiang University – Imperial College London (ZJU–ICL) database to check for potential biases between nine prediction procedures, considering a range of factors. The analysis highlights the critical importance of addressing age after driving, open and closed ends, tension versus compression, and concrete compared to steel. It also shows the hierarchy of reliability parameters associated with the alternative approaches. The “full” Imperial College pile (ICP) approach and The University of Western Australia (UWA) approaches are found to have significant advantages in eliminating potential biases. It is also argued that design load and resistance or safety factors should be varied to match the design and site investigation methods applied, as well as the loading uncertainty and degree of load cycling, which often vary between applications. Noting that predictions for base capacities Qb are inherently less reliable than for shaft Qs, especially in rapidly varying ground profiles, credible lower bound parameters (cone resistance, qc) are recommended for Qb assessment. It is also recommended that the potential effects of cycling be addressed carefully in cases that involve substantial environmental loading.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".