Development of negative skin friction on single piles: uncoupled analysis based on nonlinear consolidation theory with finite strain and the load-transfer method
Bibliographic record
Abstract
The development of negative skin friction (NSF) on single piles is investigated based on an uncoupled method of analysis with the Mikasa (1963) generalized nonlinear consolidation theory in terms of finite strain and the nonlinear load-transfer method. Predicted results are compared with results based on the conventional linear consolidation theory with infinitesimal strains. It is found that predicted development of dragload using the conventional consolidation theory is slightly greater and conservative compared to that using the nonlinear consolidation theory based on effective stress (β method). Effective stress predictions using the conventional theory are larger due to the faster dissipation of excess pore pressures, with the assumption of constant coefficient of consolidation and permeability. However, since the relative displacements required to mobilize the ultimate skin friction are small, and piles are usually installed near the final stages of soil consolidation, the differences in the predictions for the development of dragload on piles between the two consolidation theories are overshadowed. Using the uncoupled model for pile NSF, it is therefore found that the most significant factor for the estimation of dragload and downdrag is the proper selection of the β value rather than the consolidation theory used.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".