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Record W2143480534 · doi:10.1061/40962(325)12

Effective Stress Analysis and Set-up for Shaft Capacity of Piles in Clay

2008· article· en· W2143480534 on OpenAlexaffabout
Bengt H. Fellenius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsRoyal Alexandra Hospital
Fundersnot available
KeywordsDissipationOverburdenGeotechnical engineeringPore water pressureClay soilPlasticityOverburden pressureEffective stressStress (linguistics)Shear (geology)Materials scienceMechanicsStructural engineeringGeologySoil waterThermodynamicsPhysicsComposite materialSoil scienceEngineering

Abstract

fetched live from OpenAlex

A case history of repeated dynamic and static loading tests in Alberta on two pipe piles during dissipation of driving-induced pore pressures is presented together with three reanalyzed published case histories involving similar records. The four case histories demonstrate that, for each case, the same effective-stress proportionality coefficients, beta-coefficients, fit the capacities at different degrees of dissipation of excess pore pressures. For two of the test sites, the beta-coefficients back-calculated from the tests differed considerable from the values determined from the soil plasticity relation, while for two, the agreement is good. For one case, the back-calculated shaft resistance agreed well with the values of vane shear strength, while a less good agreement was found for the other tests. Neither case showed good agreement was found for methods combining undrained shear strength and effective overburden stress. Capacity calculations for two cases employing methods based on CPT soundings gave excellent agreement with one test and a poor agreement with the other. The increase of capacity due to aging after dissipation of excess pore pressures did not agree with cited recommendation for calculations of aging effect.

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 score0.096
Threshold uncertainty score0.312

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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations36
Published2008
Admission routes2
Has abstractyes

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