Performance Evaluation of SPT-based Design Methods for the Axial Capacity of Driven Piles in Glacial Deposits
Bibliographic record
Abstract
This paper presents an evaluation on the performance of seven SPT-based design methods for the axial capacity of steel piles driven in glacial deposits. This study is based on a database of pile load tests conducted in the province of Ontario, Canada. First, the performance these SPT-based design methods was evaluated for a total of 52 driven steel H or pipe piles. Most methods overpredicted on average by a factor of 1.01 to 2.14 with a large coefficient of variation (COV) ranging from 58.3 % to 97.4 %. Then, resistance factors for the ultimate limit state were calibrated using Monte Carlo simulation for these methods. The resistance factors are found to be generally low, ranging from 0.05 to 0.66. This study shows the risks and challenges of a pile design in glacial deposits.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".