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Record W2136027035 · doi:10.1139/t99-061

Analysis of calibration chamber plate load tests

2000· article· en· W2136027035 on OpenAlexvenueno aff
Junhwan Lee, Rodrigo Salgado

Bibliographic record

VenueCanadian Geotechnical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersFederal Highway AdministrationIndiana Department of Transportation
KeywordsFinite element methodCalibrationPileSettlement (finance)Geotechnical engineeringStructural engineeringConstitutive equationBase (topology)Stress (linguistics)Rotational symmetryEngineeringGeologyMechanicsMathematicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

The estimation of base resistance is a key step in the design of piles embedded in moderately dense to dense sand. Calibration chamber plate load tests are sometimes used to investigate the base load - settlement relationship of nondisplacement piles in sand. In such tests, the sand specimens are carefully prepared to simulate the installation of nondisplacement piles. In this paper, calibration chamber tests are analyzed using the finite element method; experimental and numerical results are compared. The finite element models are axisymmetric and use a nonlinear, elastic-plastic constitutive model. Plate resistance values predicted using the finite element analysis are shown to be in good agreement with measured values, which validates the proposed numerical model. Questions regarding the existence of calibration chamber size effects have not, to this date, been adequately addressed. Finite element analyses of both pile base resistance and plate resistance for sands with various relative densities and stress states show that size effects are usually small for settlement levels of interest in practice. This suggests that the use of calibration chambers in pile base capacity studies is justified.Key words: calibration chambers, plate load tests, constitutive modeling, size effects, piles, sands.

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 categoriesInsufficient 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 score0.061
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.189
Teacher spread0.183 · 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

Citations44
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207