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Record W1907844194 · doi:10.1139/cgj-2014-0176

Using piezocone dissipation test to estimate the undrained shear strength in cohesive soil

2014· article· en· W1907844194 on OpenAlexvenueno aff
F. M. Mántaras, E. Odebrecht, ‪Fernando Schnaid

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPore water pressureGeotechnical engineeringDissipationCompressibilityPenetration testEffective stressSoil waterCritical state soil mechanicsTriaxial shear testShear strength (soil)Shear (geology)GeologyMechanicsEngineeringConstitutive equationStructural engineeringThermodynamicsSoil scienceSubgradePhysicsFinite element method

Abstract

fetched live from OpenAlex

This paper describes a method developed to link the measured piezocone dissipation excess pore-water pressure (Δu) to the soil undrained shear strength (s u ). In cohesive soils, both Δu and s u are dependent on the same variables (compressibility, stress state, stress history), which allows them to be related by the theoretical cavity expansion – critical state framework. A mathematical derivation is presented to demonstrate that the ratio of normalized maximum excess pore pressure and the normalized undrained shear strength fluctuates around a mean value, being affected by soil strength and compressibility and independent of stress history. The predicted s u values obtained from the proposed approach are calibrated against field vane shear strength in both normally consolidated (monotonic dissipation tests) and overconsolidated soils (dilatory dissipation tests). Reported results are consistent and encourage the use of the method in engineering practice. On a routine basis, s u estimated from pore pressure can be compared with values predicted from penetration resistance, adding desired redundancy for purposes of error detection when interpreting cone penetration testing with pore pressure measurement (CPTU) data.

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)0.001
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 score0.484
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.009
GPT teacher head0.240
Teacher spread0.230 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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