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Record W2947485500 · doi:10.1139/cgj-2018-0656

Shallow penetrometer tests: theoretical and experimental modelling of penetration and dissipation stages

2019· article· en· W2947485500 on OpenAlexvenueno aff
M.A. Schneider, Sam Stanier, David White, Mark Randolph

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersWoodsideAustralian Government
KeywordsDissipationGeotechnical engineeringPenetrometerConsolidation (business)CoringSubmarine pipelinePenetration testEngineeringSubseaPenetration (warfare)GeologySoil waterDrillingMechanical engineering

Abstract

fetched live from OpenAlex

Shallow penetrometers are devices that penetrate into and measure the properties of surficial offshore sediments via multi-phase tests involving penetration, dissipation, and rotation stages. In fine-grained soils such as silts and clays, these testing stages yield undrained strength, consolidation, and friction properties relevant to subsea pipeline and shallow foundation design. This paper describes toroid and hemiball devices of the scale for use in box-core samples and associated interpretation methods for the penetration and dissipation stages. The aim of the paper is to provide all tools needed to design and interpret these tests. New large-deformation finite element (LDFE) dissipation solutions are presented, which can be used for back-analysis of the dissipation stage. Results of an extensive laboratory proof testing exercise in kaolin clay, for both the hemiball and toroid penetrometers, are also reported. These results highlight the potential of the two devices to quickly and economically assess strength and consolidation characteristics of fine-grained sediments in box-core samples recovered to the deck of a site investigation vessel.

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 score0.026
Threshold uncertainty score0.464

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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