Axial compressive capacity of helical piles from field tests and numerical study
Bibliographic record
Abstract
The compressive capacity of helical piles in sand and clay is investigated by means of field testing and numerical modeling. The numerical models are conducted using the computer program ABAQUS and are calibrated and verified using full-scale load testing data. The calibration was accomplished by using reasonable assumptions regarding soil–pile interaction and soil parameters reported from the literature. The model was verified by comparing its predictions with observed load–displacement curves obtained from full-scale pile load tests. The verified numerical model was used to perform a parametric study considering different pile configurations and soil parameters to evaluate the compressive capacity and load-transfer mechanism of helical piles. The compressive capacity obtained from the numerical models is compared with that obtained from existing theoretical methods for calculating the capacity. It is found that the predictions of theoretical equations for piles in cohesionless soil vary largely depending on the choice of bearing capacity factors and proper failure criteria. The interaction of closely spaced helices on the capacity of a helical pile is also evaluated. A bearing capacity reduction factor, R, and helix efficiency factor, EH, are proposed to evaluate the compressive capacity of helical piles in cohesionless soil considering an industry-acceptable ultimate load criterion corresponding to settlement equal to 5% of helix diameter, D.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".