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Record W2598872947 · doi:10.1061/9780784480489.016

Volumetric Strain in Non-Plastic Silty Sand Subject to Multidirectional Cyclic Loading

2017· article· en· W2598872947 on OpenAlexaboutno aff
Lopamudra Bhaumik, Cassandra J. Rutherford, Alfonso Cerna-Díaz, Scott M. Olson, Ozgun A. Numanoglu, Youssef M. A. Hashash, 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
KeywordsStress pathGeotechnical engineeringShear (geology)Settlement (finance)Overburden pressureOverburdenGeologyShear stressMaterials scienceSimple shearStrain (injury)Structural engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Structures founded on dense, compacted sands are currently designed based on the shear stress–shear strain (γcs)–volumetric strain (εv) response of clean sands primarily obtained from unidirectional element tests. This may lead to underestimation of εv under multidirectional seismic loads and incorrect settlement estimation for sands with fines. This paper presents results from first of a kind strain-controlled unidirectional (sinusoidal) and bidirectional (circular, figure-8) cyclic simple shear tests. Clean Ottawa sand and a non-plastic Mississippi River silty sand were tested at modified Proctor relative compactions (RC) of 92 to 99%. At the same RC, presence of non-plastic fines decreased εv. Volumetric strain under bidirectional loading normalized by εv under equivalent unidirectional loading (comparable γcs, RC, overburden pressure) was found to be a function of the number of loading cycles, load path and γcs. A figure-8 loading path yielded higher settlement than an equivalent circular loading path.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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.

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

Citations10
Published2017
Admission routes1
Has abstractyes

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