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Record W2915297063 · doi:10.1520/gtj20170413

Laboratory Simulator for Geotechnical Penetration Tests

2019· article· en· W2915297063 on OpenAlexaff
Michael Ghali, Mourad Karray

Bibliographic record

VenueGeotechnical Testing Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsHydro-QuébecUniversité de Sherbrooke
Fundersnot available
KeywordsGeotechnical engineeringVoid ratioParametric statisticsPenetration testGranular materialInterlockingStress pathPenetration (warfare)EngineeringStructural engineeringMaterials scienceMathematicsSubgradePlasticityStatistics

Abstract

fetched live from OpenAlex

Abstract The most widely used methods for the in situ investigation of the mechanical characteristics of soil are field penetration tests such as the standard and cone penetration tests, which apply several empirical correlations. Many geotechnical researchers have tried to improve the empirical correlations by analyzing comparisons of field penetration tests and laboratory simulations, as well as virtual numerical simulations. However, geotechnical parameters such as relative density, void ratio range, grain size distribution, angularity, mean particle size, uniformity coefficient, effective overburden stress, and mean confinement stress have interlocking effects. Consequently, the use of calibration chambers, which can simulate ideal conditions for the soil in the laboratory, are essential. The authors have developed an axisymmetric innovative simulator system, not only to perform parametric studies on the aforementioned parameters but also to simulate a range of field conditions under substantially controlled boundary conditions. This article also presents results for a series of calibration tests to prove its applicability on nonplastic silts, sands, and granular materials. Repetitive tests, under identical testing circumstances, verified the accuracy, efficiency, and durability of the system. The current work also correlates with the results of comparative studies to investigate the effectiveness of the simulations.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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