Interpreting CAPWAP results for driven piles with corrections for setup and residual loads
Bibliographic record
Abstract
Evaluation of pile capacity using dynamic testing and Case Pile Wave Analysis Program (CAPWAP) analysis has been shown to provide reasonable measurements of capacity and separates the shaft and tip resistance. It is a common method for verifying pile capacities; however, consideration of setup and presence of residual loads at the time of testing are necessary for interpreting the resistance distribution. A test pile program was conducted that included dynamic testing of piles at the end of initial driving and early restrikes to measure the rate of setup. The piles were instrumented to measure residual stresses. The shaft resistance and tip stiffness were interpreted from analyzing multiple blows and corrected for residual stresses. The results from CAPWAP were compared to measurements from static load tests. It was found that correcting CAPWAP results for residual loads resulted in better interpretation of the rate of setup and resistance distribution. Analyzing successive blows with CAPWAP provided an improved estimate of the tip load–movement curve by extrapolating the increase in tip capacity with penetration. The results demonstrate the importance of correcting CAPWAP results for residual stresses to gain a better understanding of the pile capacity, resistance distribution, and tip stiffness for design purposes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".