Experimental investigation on the installation and loading performance of model-scale deep helical piles in very dense sand
Bibliographic record
Abstract
The use of helical piles has grown over the years owing to different advantages, such as large uplift capacity due to the anchor effect of the helix and installation torque-capacity correlation. Although the use of helical foundations is expanding worldwide, some key aspects fundamental to the design are not well understood to date. Practical experience indicates that the installation forces and loading performance of helical piles in sand are dependent on the helices characteristics and confining stresses. Therefore, for a better understanding of these dependencies, the effects of the helix-to-shaft diameter ratio (wing ratio) and vertical confining stress on the installation torque and forces, and on the uplift and compression capacities of helical piles, were evaluated from nine calibration chamber tests, conducted on instrumented single-helix piles in very dense sand. Among other findings, this study indicated that for a certain shaft diameter (i) the wing ratio influences the installation torque, but does not affect the installation vertical force; (ii) the ultimate uplift pressure mobilized on the helix decreases with the increase of the wing ratio; (iii) the growth rate of the helix bearing resistance with the confining pressure, for vertical stresses higher than 100 kPa, becomes reduced for piles with a larger wing ratio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".