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Record W4388016751 · doi:10.1139/cgj-2022-0639

The role of physicochemical processes in aging of shaft friction of driven steel piles in sand

2023· article· en· W4388016751 on OpenAlexvenueno aff
Eduardo Bittar, Barry Lehane, Hao Zheng

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringGalvanizationPileFriction angleDilation (metric space)Materials scienceShear (geology)MetallurgyDilatantGeologyDry sandComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Several studies have reported substantial increases in the shaft capacity of driven steel piles in the months following installation. This study investigates factors influencing this time dependence of shaft capacity by conducting a series of field tests on piles and a parallel series of interface shear tests using a newly developed apparatus. The piles and interfaces used in the experiments employ mild steel, stainless steel, and galvanized steel, while the aging periods allowed in the laboratory and field were 1 and 3 years, respectively. Chemical analyses of the crusts that developed at the sand–steel interfaces are reported. It is shown that the aging characteristic of sand-steel friction depends on the relative contributions of interlocking and dilation but is controlled by dilation at the crust–sand interface adjacent to the shaft of a driven pile. There is no gain in shaft friction with time in dry sand or for piles with non-reactive steel. The operational friction angle for mild steel piles in moist or saturated sand is the soil–soil friction angle.

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 score0.191
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.210
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

Citations6
Published2023
Admission routes1
Has abstractyes

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