Pile-Driving Mechanics at the Base as Informed by Direct Measurements
Bibliographic record
Abstract
Determination of the base contribution to total pile capacity is an important aspect in the design of end-bearing piles. For driven piles, dynamic load testing and signal matching are the predominant approach for estimating total pile capacity. This includes separating the base and shaft resistance, and differentiating between static and dynamic contributions. Despite its widespread usage, the approach is susceptible to uncertainty and nonuniqueness of solutions. The new instrumented Becker Penetration Test (iBPT) configured as a reusable test pile (RTP) is capable of directly measuring the dynamic pile response at the base and along the shaft during driving. In this paper, RTP measurements at the base are presented and used to guide the selection of models and parameters available in signal-matching methods. The direct measurements at the base depict pile driving as a steady penetration process with unload–reload cycles, and consistency of locked-in residual force between subsequent blows. The results show that when fundamental mechanical constraints are satisfied, simple existing models are adequate for capturing the measured response, and uncertainty and nonuniqueness at the base are curbed.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".