MétaCan
Menu
Back to cohort
Record W2737767292 · doi:10.1680/jgeot.16.p.312

CPT calibration and analysis for a carbonate sand

2017· article· en· W2737767292 on OpenAlexaff
Daniela Giretti, K. Been, V. Fioravante, Stephen E. Dickenson

Bibliographic record

VenueGéotechnique · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsVoid ratioCentrifugeGeotechnical engineeringCalibrationCarbonateGeologySubmarine pipelineCompressibilityVoid (composites)MineralogyMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

This paper describes a project-based cone penetration test (CPT) calibration on a carbonate sand fill that was hydraulically placed and subsequently densified by vibrocompaction. The project involved the development of an artificial island constructed offshore from the United Arab Emirates for oil and gas production. Carbonate sands have crushable grains and can be significantly more compressible than silica sands, so it was determined that the semi-empirical CPT-based correlations for silica sands were not applicable and a soil-specific calibration was needed for post-densification characterisation of density, shear strength and compressibility. The CPT calibration investigation was primarily undertaken in a centrifuge, and then checked with supplementary tests in a large calibration chamber. In this paper, analysis of the calibration data follows three different threads for comparison with more typical siliceous sands: (a) relationships between CPT tip resistance, void ratio and vertical effective stress; (b) a relationship between CPT tip resistance and state parameter; and (c) an approach based on cavity expansion theory, which provides predictive capability for the CPT once soil properties have been measured in the laboratory. The test results are compared with silica sands, in terms of void ratio or the state parameter ψ.

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: none
Teacher disagreement score0.964
Threshold uncertainty score0.404

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 head0.222
Teacher spread0.210 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGéotechniqueSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207