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Record W1978146915 · doi:10.1139/t05-030

Cone tip resistance in sand: modeling, verification, and applications

2005· article· en· W1978146915 on OpenAlexvenueno aff
Mohammad Mehdi Ahmadi, Peter Byrne, R. G. Campanella

Bibliographic record

VenueCanadian Geotechnical Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringMohr–Coulomb theoryCalibrationPenetration (warfare)GeologyCone penetration testBoundary value problemStress (linguistics)Penetration depthComputer simulationNumerical analysisPenetration testMechanicsMaterials scienceStructural engineeringEngineeringFinite element methodMathematicsPhysicsOpticsMathematical analysis

Abstract

fetched live from OpenAlex

A numerical modeling procedure is presented to evaluate cone tip resistance in sand. The procedure involves a moving boundary simulating cone penetration. The soil is modeled as a Mohr–Coulomb elastic–plastic material with stress-dependent parameters. The procedure is verified by comparing predicted numerical values of cone tip resistance with published experimental measurements from calibration chamber tests. The selected database consists of 59 calibration chamber tests on Ticino sand with different relative densities, overconsolidation ratios, stresses, and boundary conditions. Several applications of the modeling procedure are also presented. The computer program FLAC is used to carry out the analysis.Key words: cone tip resistance, numerical modeling, sand, calibration chamber, Mohr-Coulomb, in situ horizontal stress.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 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

Citations51
Published2005
Admission routes1
Has abstractyes

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