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Record W2602971997 · doi:10.1061/9780784480489.013

Free-Field Cyclic Response of Dense Sands in Dynamic Centrifuge Tests with 1D and 2D Shaking

2017· article· en· W2602971997 on OpenAlexaboutno aff
Alfonso Cerna-Díaz, Scott M. Olson, Ozgun A. Numanoglu, Youssef M. A. Hashash, Lopamudra Bhaumik, Cassandra J. Rutherford, Thomas J. Weaver

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersU.S. Nuclear Regulatory Commission
KeywordsCentrifugeGeotechnical engineeringShear (geology)GeologyOverburden pressureShear stressSoil waterMaterials scienceSoil scienceComposite materialPetrology

Abstract

fetched live from OpenAlex

The seismic performance of many nuclear power plant (NPP) structures depends on the cyclic shear stress-shear strain-volumetric strain behavior of dense, compacted coarse-grained soils used to support their foundations. However, when these deposits are thick, even relatively small volumetric strains can result in nontrivial settlements that can impact NPP structures. Here, we describe the results from dynamic centrifuge tests performed on thick layers (prototype thicknesses of 10.25 and 20.5 m) of saturated dense (relative density, Dr~95%), Ottawa sand. Models were excited using unidirectional (1D) and bidirectional (2D) broadband motions (Arias intensities ranged from 0.1 to 5 m/s). Centrifuge test results indicate minor differences between 1D and 2D response spectra. In contrast, 2D shaking in dense sands caused increases in porewater pressure (PWP) generation and volumetric strains (εv) of approximately 200% compared to 1D shaking. These increases in PWP and εv were considerably larger than observed by others for loose 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2017
Admission routes1
Has abstractyes

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