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Record W4390690967 · doi:10.1139/cgj-2023-0098

Bidirectional static loading tests on barrette piles. A case history from Ho Chi Minh City, Vietnam

2024· article· en· W4390690967 on OpenAlexvenueno aff
Tan Nguyen, Bengt H. Fellenius

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersFoundation of Science and Technology Development of Ton Duc Thang UniversityStrong
KeywordsPileGeotechnical engineeringGeologyHead (geology)Dynamic load testingSiltGeomorphology

Abstract

fetched live from OpenAlex

Bidirectional static loading tests were conducted on two strain-gage instrumented barrettes installed to 72 m depth in Ho Chi Minh City, Vietnam. The barrettes were to support a 16-storey building with 5 basements. The soil profile comprised layers of medium coarse to fine sand, medium clay, firm to stiff clayey soil, and dense sandy silt. The region is experiencing an ongoing land subsidence affecting the upper about 40 m of soil and, on average in the city, the ground surface is currently settling 16 mm/year. The test records were processed by means of effective stress analysis to provide the axial pile force distribution, load transfer functions, and equivalent head-down load–movement curve. The analysis was then used to obtain the equivalent pile-head load–movement response adjusted to the planned 22 m deep basement excavation. Load transfer functions were back-calculated from the test records and indicate that the construction will show somewhat large load-transfer movement. However, because the equilibrium plane will be below the subsiding layers, below 40 m depth, downdrag is not expected to affect the building. The load response of the barrettes is compared to the results of a bidirectional (BD) loading test on a 1.8 m diameter bored pile at an adjacent project.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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.

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

Citations9
Published2024
Admission routes1
Has abstractyes

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