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Record W4412580769 · doi:10.1016/j.istruc.2025.109749

Axial capacity of helical piles-A state of the art review of field test and design guidelines

2025· article· en· W4412580769 on OpenAlexafffundabout
Debarshi Das, A. H. M. Muntasir Billah

Bibliographic record

VenueStructures · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Calgary
FundersMitacsUniversity of Calgary
KeywordsTest (biology)Field (mathematics)Structural engineeringEngineeringState (computer science)Geotechnical engineeringGeologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.245
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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