Relationship between installation torque and uplift capacity of deep helical piles in sand
Bibliographic record
Abstract
The empirical torque correlation factor (KT), which relates the uplift capacity to the installation torque of helical piles, is routinely used as an on-site instrument for quality control with this type of foundation. This paper presents a theoretical relationship between uplift capacity and installation torque of deep helical piles in sand. An experimental program, including centrifuge and direct shear interface tests, was carried out to validate this expression. The experimental results were compared with the values predicted by the suggested approach and showed good agreement. As the developed model depends on the residual interface friction angle (δr) between the helix surface and the surrounding sand, results of δr, extracted from different sand samples, are presented for use in this suggested relationship on site. Also, the values of KT found in this work were compared with those of field and laboratory tests on helical piles in sand reported in the literature. From this analysis, it was found that the measured values of KT decrease with an increase in pile dimensions and, in most of cases, with an increase in sand friction angle. These results were explained by the presented model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".