Axial capacity of helical piles-A state of the art review of field test and design guidelines
Bibliographic record
Abstract
Helical piles (HPs) have been widely utilized in North America and other regions to support various superstructures, including power transmission towers, residential and commercial buildings, and pedestrian bridges. This study provides a comprehensive overview of various experimental investigations, field testing, and numerical studies that have been undertaken to analyse the behaviour of HPs in different soil conditions and under different loading scenarios. Additionally, this article delivers a concise overview of the design features of HPs, including current design recommendations, their benefits, and limitations, as well as HP's axial and lateral capacity estimate techniques. It also compares several design codes for predicting the axial capacity of HPs, including CFEM (Canada), AC 358 (USA), AS 2159 (Australia), and Practice Note-28 (New Zealand), highlighting their key similarities and differences. Analysis of existing field test data indicates that HPs can achieve capacities of up to 2500 kN in compression and 2000 kN in tension. Moreover, in recent times, HPs have been used in relatively few bridge construction projects in North America. This paper summarizes existing design practices, compares HP capacities reported in the literature, and identifies current trends in the application of HPs as an alternative deep foundation system in bridge foundations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".