Uncertainty of Driven Pile Capacity using Dynamic Methods
Bibliographic record
Abstract
Dynamic pile load testing (i.e., PDA and CAPWAP) was performed during the driving of six piles at two sites included large and low displacement H-piles, and actual pile capacity was determined during the end of drive.The load test provided an opportunity to compare pile design techniques to measured pile performance.The soils at one of presented sites prevent the pile driving process from being completed and the required pile length and capacity were not achieved due to early refusal.Therefore, the engineers redesigned the deep foundation system, whereby the large displacement prestressed concrete piles (PCP's) were replaced with low-displacement steel H-piles.In this paper, seven dynamic methods for predicting axial pile capacity of driven piles are investigated and summarized.The dynamic formulas included Eytelwein, Modified ENR, Janbu, Danish, Navy-Mckay, Gate, and PCUBC.The measured pile capacities were compared to the predictive capacities to evaluate which predictive method would be best suited for estimating the pile capacity at site where such difficult soils may encountered.The evaluation revealed that the pile dynamic formulas are mostly underpredicting pile capacity.Amongst the seven methods, the Danish method gave the most realistic values of the pile capacity.The predictions using the Gates and Modified ENR methods were found to be overly lower than the measured values and was ranked least desirable amongst the methods.The predictions at site where early refusal was encountered, found to be overly lower than the measured values.However, concrete piles were replaced by H-pile.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".