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Record W2009726570 · doi:10.1139/t08-025

Evaluation of soil state from SBP and CPT: A case history

2008· article· en· W2009726570 on OpenAlexaffvenue
Mohsen Ghafghazi, Dawn Shuttle

Bibliographic record

VenueCanadian Geotechnical Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterpretation (philosophy)Geotechnical engineeringState (computer science)Finite element methodSimple (philosophy)Structural engineeringMathematicsComputer scienceEngineeringAlgorithm

Abstract

fetched live from OpenAlex

The cone pressuremeter test (CPT) is widely used to determine the in situ “state” of cohesionless soils. However, although the CPT is simple, inexpensive, and accurate the subsequent interpretation contains substantial uncertainties even with modern approaches. Self-bored pressuremeters (SBP) have the opposite attributes. Obtaining good SBP data is difficult in sands, but the subsequent evaluation can be rather precise. This paper compares estimates of the in situ state parameter, ψ, from CPT and SBP tests carried out in a uniform hydraulic fill. This case history is unusual in that (i) the fill was well controlled and uniform, (ii) comprehensive laboratory strength data exists, (iii) the CPT was calibrated for the fill in a large chamber, and (iv) good SBP data exist. These SBP and CPT tests are independently analyzed using a calibrated critical state model implemented in a large strain finite element code. The effects of ageing and fabric are considered. The resulting most probable in situ state parameters for the fill from the CPT are close to those derived from the SBP. Although not proof of accuracy (validation) of either test, since ground truth is not known, the results lend support to the adequacy of the interpretation methodology used for both. Further improvements are discussed.

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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.985

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 integrity0.0000.001
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 head0.019
GPT teacher head0.198
Teacher spread0.179 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

Explore more

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