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Record W4410800969 · doi:10.1139/cgj-2025-0081

Investigation of engineering-scale testing and bearing capacity calculation method for rotary screw special-shaped piles

2025· article· en· W4410800969 on OpenAlexvenueno aff
Pengfei Ma, Zhaoyang Deng, Yulong Zhang, Yu Huang, Chao Yuan

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsBearing capacityGeotechnical engineeringScale (ratio)Bearing (navigation)EngineeringScale effectsStructural engineeringForensic engineeringGeologyComputer science

Abstract

fetched live from OpenAlex

This paper investigates the bearing performance of rotary drilling screw special-shaped piles, a novel foundation solution designed to enhance load-bearing capacity in geotechnical engineering. Combining engineering-scale tests with numerical simulations, the study evaluates the interaction between piles and surrounding soil, with particular focus on the effect of thread geometry. The proposed orthogonal simulation model assesses the influence of key design parameters such as thread height, pitch, and pile length on compressive bearing capacity and end resistance. The results demonstrate that the thread structure increases the interaction area between the pile and soil, improving side friction resistance and overall bearing capacity. A method for calculating the bearing capacity of screw special-shaped piles is proposed, based on Meyerhof’s theory for deep foundations, which is validated through experimental data. This research provides valuable insights for optimizing pile foundation design and offers a reliable approach for calculating bearing capacity. The findings highlight the potential of screw special-shaped piles to enhance structural stability and material efficiency in challenging geotechnical conditions, offering a solid foundation for future studies on advanced pile systems in civil engineering.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.509

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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