MétaCan
Menu
Back to cohort
Record W2477867564 · doi:10.1680/jgrim.15.00017

Estimating relative density of sand with cone penetration test

2016· article· en· W2477867564 on OpenAlexaff
Abouzar Sadrekarimi

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Ground Improvement · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsCone penetration testRelative densityPenetration testGeotechnical engineeringCompactionLiquefactionSoil scienceGeologyPenetration (warfare)Environmental scienceMathematicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

As a result of difficulties in obtaining undisturbed samples in cohesionless soils, empirical correlations based on cone penetration test (CPT) results, often developed from calibration chamber experiments, are widely used for determining many soil parameters for geotechnical investigation. This paper describes the application of 19 reduced-scale calibration chamber CPTs for examining the empirical correlations used for predicting the relative density of loose to medium-dense sands. A reduced-scale subtraction cone with an apex angle of 60° and a net area ratio of 0·75 was used in the laboratory tests. Current methods for estimating relative density present very large differences among themselves as well as with the experiments of this study, rendering them meaningless for general application in all sands. The state parameter is suggested as a more reliable and universal alternative to relative density for estimating the degree of compaction of any sand deposit from CPT data. It is demonstrated that an empirical method provides reasonable estimates of the state parameter for the CPT experiments of this study. Compared with relative density, the state parameter method can be particularly useful for examining the liquefaction susceptibility of sands.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.436

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Ground ImprovementSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207