Evaluation of prediction models for tip resistances of rock-socketed drilled shafts
Bibliographic record
Abstract
This study compiled a database called CYCU/RockTip/51 consisting of 51 rock-socketed drilled shafts installed at different sites worldwide, covering a wide variety of rock properties and shaft geometries. The tip resistances from seven representative prediction models were compared with the measured values. These measured values were obtained from the load–displacement curves of field load tests using three interpretation criteria. It was found that the prediction models of Teng, Coates, Rowe and Armitage, and ARGEMA overpredicted the measured capacity, while Zhang and Einstein, Vipulanandan et al. and Zhang are less biased. These tip prediction models also could be classified according to displacement requirements. The proposed tip prediction models of this study are presented based on different interpretation methods and the displacement ranges that they are mobilized. Finally, the normalized load–displacement curve was fitted to the hyperbolic curve with two model parameters ( a and b). The parameters a and b define the reciprocal of the initial slope and the asymptotic resistance, respectively. The statistics of ( b, a) for rock-socketed drilled shafts in CYCU/RockTip/51 are mean = (0.76, 1.09), COV = (0.13, 0.59), and correlation coefficient = −0.79. These statistics are useful for reliability-based design.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".