Capacity evaluation of voided driven piles using embedded data collectors
Bibliographic record
Abstract
This paper presents an application of a method that will be implemented in the embedded data collector (EDC) system in the near future, to estimate the capacity of driven piles with a combined solid and voided cross section. Data from accelerometers and strain gauges located in the solid sections at both the top and the bottom of a pile are used to independently estimate the pile’s skin friction and tip resistance. Wave propagation along the pile is modeled as a one-dimensional wave equation, with a nonuniform cross section and with nonlinear static skin friction and viscous-damping soil resistances acting along multiple segments of the pile. The static skin friction is extracted by least-squares fitting of computed particle velocities with measured data at both the top and the bottom of the pile. The pile tip is modeled as a nonlinear single degree of freedom with viscous damping. Static tip resistance (nonlinear stiffness–displacement relationship) is extracted by least-squares fitting of the predicted total force and energy with the measured tip data. The new EDC method was applied to four combined solid–voided cross section driven piles with capacities varying from 2800 to 6700 kN. The results of the data evaluated with the new EDC method are consistent with those from the static load tests to within 15%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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".