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Record W2329329576 · doi:10.1061/9780784479742.122

Effect of Shaking History on the Cone Penetration Resistance and Cyclic Strength of Saturated Sand

2016· article· en· W2329329576 on OpenAlexaboutno aff
Kathleen M. Darby, Jaclyn D. Bronner, Ana Maria Parra Bastidas, Ross W. Boulanger, Jason T. DeJong

Bibliographic record

VenueGeotechnical and Structural Engineering Congress 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCentrifugePenetrometerCone penetration testPenetration (warfare)Penetration testGeotechnical engineeringLiquefactionMaterials scienceGeologyEngineeringSoil waterPhysicsSoil science

Abstract

fetched live from OpenAlex

The effect of shaking history on cone penetration resistance, cyclic resistance ratio, and their correlation to each other for saturated sand is examined using centrifuge model tests. Prior laboratory and centrifuge modeling studies have shown strain history can have a strong effect on the cyclic strength of sand, but data describing how these effects track with cone penetration resistance are lacking. The effects of shaking history on cone penetration resistance and cyclic strength are investigated using centrifuge models of saturated Ottawa sand on a 1-m radius centrifuge with a 6-mm diameter cone penetrometer. The centrifuge models are subjected to a series of shaking events at progressively increasing amplitudes until liquefaction is triggered. This motion is repeated until the sand no longer liquefies. Cone penetration tests are performed before any shaking, after liquefaction is triggered, and after liquefaction no longer occurs. Inverse analyses of accelerometer array data are used to compute profiles of dynamic shear stresses and strains. The results are used to examine the effects of prior strain history on cone penetration resistance, cyclic resistance ratio, and their correlation to each other. The centrifuge test results are also compared with a case history-based liquefaction triggering correlation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.536

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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