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Record W4376129526 · doi:10.1139/cgj-2022-0102

Seasonal variation of the shaft resistance of CFA piles in unsaturated soil: a case study

2023· article· en· W4376129526 on OpenAlexvenueno aff
João Gabriel Mercio Xavier Zacarin, Bruno Oliveira Da Silva, Cristina de Hollanda Cavalcanti Tsuha, Orêncio Monje Vilar

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringPileSuctionSoil waterEnvironmental scienceGeologyEngineeringSoil science

Abstract

fetched live from OpenAlex

Excessive settlements or failure of piles observed in some Brazilian unsaturated soil sites indicates that the shaft resistance can vary with the seasonal variability of top soil water content, especially in the case of foundations of transmission towers or installed at the periphery of buildings, situations in which the soil is more susceptible to suction fluctuation due to the higher exposure to rainfall action. The objective of the research described in this paper was to provide field evidence with the purpose of alerting practising engineers and to help researchers to better understand the contribution of matric suction on pile capacity. For this aim, the seasonal variability of the shaft resistance of Continuous Flight Auger (CFA) piles in a typical Brazilian unsaturated tropical soil was evaluated by performing axial tensile load tests on eight instrumented CFA piles in different periods of the year. Soil suction was monitored throughout the period of tests, and the results indicate that the shaft resistance of the pile upper part varies considerably during the year and tends to decrease with decreasing soil suction. This work provides valuable insights into the application of unsaturated soil mechanics, which is usually disconnected to classical geotechnical practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations7
Published2023
Admission routes1
Has abstractyes

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