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Record W4414989224 · doi:10.3390/buildings15193620

Axial Compression and Uplift Performance of Continuous Helix Screw Piles

2025· article· en· W4414989224 on OpenAlexaff
Ahmed Mneina, M. Hesham El Naggar, Osama Drbe

Bibliographic record

VenueBuildings · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
Fundersnot available
KeywordsPileCompression (physics)Helix (gastropod)Shear (geology)Failure mode and effects analysisTension (geology)Ultimate tensile strengthStrain gaugeTorque

Abstract

fetched live from OpenAlex

This study investigates the axial performance of continuous helix screw piles compared to helical piles through full-scale compression and tension load testing in layered soils. Twenty-three piles were installed and tested. The results demonstrate that screw piles can achieve considerable axial capacity with lower installation torque than helical piles, particularly under tensile loading. The capacity-torque relationship for screw piles was more consistent across both compression and tension, likely due to reduced soil disturbance from the smaller helix projection. Strain gauge measurements indicated that screw piles act primarily as friction piles with the threaded shaft carrying most of the load, especially in stiff clay. On the other hand, the smooth portion of the pile shaft contributed only marginally to resistance in compression and none in tension. The calculated capacity based on theoretical equations aligned well with field results in compression, with screw piles best represented by cylindrical shear failure in sand and a combination of cylindrical shear and individual bearing failure in clay. However, there is greater variability between calculated and measured uplift capacity, possibly due to soil disturbance effects. Additionally, the commonly used helix spacing ratio (S/D) was found to be less applicable to screw piles in predicting failure mode due to their smaller shaft-to-helix diameter difference.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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 designSimulation or modeling
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 routes1
Has abstractyes

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