Large-diameter helical pile capacity – torque correlations
Bibliographic record
Abstract
Large-diameter helical piles are utilized increasingly to support heavy structures. Both the magnitude of the required installation torque and the pile capacity can be directly attributed to the soil shearing resistance developed over the embedded area of the pile including the shaft and helical plates. Hence, the pile capacity can be correlated to installation torque. Such correlations are widely used in the helical pile industry as a means for quality control and quality assurance. In the current study, a total of 10 test piles were installed while monitoring the installation torque continuously with depth. The recorded installation torque profiles were demonstrated to be accurate and repeatable. Field pile load tests were conducted and their results were analyzed to determine the interpreted ultimate capacity of the test piles. The results demonstrate that the ultimate capacity of large-diameter helical piles can be interpreted from pile load test data employing the failure criteria proposed by Elkasabgy and El Naggar in 2015 and Fuller and Hoy in 1970. The measured installation torque and corresponding ultimate capacity values were employed to define torque–capacity correlation (Kt) based on embedded pile area. It was demonstrated that the proposed Kt is suitable for large-diameter helical piles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".