Lessons from Test and Production Programmes for Driven Piles in Sand
Bibliographic record
Abstract
This case study outlines an installation and loading test programme conducted for foundation design at an oil sands mine in Alberta, Canada. Design was for approximately 10,000 driven steel piles of various sizes founded in dense glacial deposits of sand and silty sand. Included are results from pre-construction loading tests (9 static axial tests, 56 high strain dynamic test measurements) and construction QA/QC (7 static axial tests, 5% of piles with high strain dynamic test measurements). Only 5% of piles had damage when pile shoes were used versus 46% without shoes. When piles were driven through frost without pre-drill 83% were damaged. Design curves using a modified API method provided a capacity ratio of 1.0 for dynamic tests during pre-construction versus a range of 0.4 to 0.9 during construction. It was found that at a remote site there is more value-added increasing the installation and loading test programme scope versus the subsurface investigation. Also, when using an API design approach, extrapolating to diameters and lengths other than those tested led to skewed results; the general trend was over-conservative design for smaller diameter piles and under-conservative design for larger diameter piles as relative depth increased.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".