Dynamic soil parameters and Case damping for high-capacity long H-piles
Bibliographic record
Abstract
Uncertainty exists in signal-matching techniques. The quake and damping obtained may not be the actual response of the soil. In this paper, the final sets, strain gauge readings, pile driving analyzer, and Case pile wave analysis program of 12 high-capacity long H-piles at the end of initial driving as well as two of them at restrike are studied. Measured and deduced data show that the soil response underneath the pile toes has limited movement and yielding despite the piles being set using very heavy hammer rams and large ram drops. The quake and damping decrease with increased shearing strain and shearing stress, but are influenced by pile whipping, rebounded stress wave, and load-transfer mechanism. The lumped Case damping factor decreases with increased side resistance to total resistance ratio. This factor can decrease or increase with time due to changes in the load-transfer mechanism after set-up, thus affecting the proportion of viscous damping of soil along the pile shaft and at the pile toe. A Case damping model is proposed that approximates the lumped Case damping factor as the sum of hysteretic damping of the pile and viscous damping of the surrounding soil. The effects of variation in load distribution and set-up along the pile shaft in layered soils and incomplete mobilization of soil at the pile toe on the Case damping factors are explained.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".