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Record W2791971434 · doi:10.1139/cgj-2017-0529

Shear modulus reduction and damping ratio curves for earth core materials of dams

2018· article· en· W2791971434 on OpenAlexvenueno aff
DongSoon Park, Tadahiro Kishida

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsShear modulusGeotechnical engineeringLeveeDamping ratioShear (geology)PlasticityCore (optical fiber)Materials scienceGeologyShear strength (soil)Mechanically stabilized earthSoil waterComposite materialSoil scienceRetaining wallPhysics

Abstract

fetched live from OpenAlex

It is essential to obtain shear modulus reduction and damping ratio curves to perform dynamic analyses of earth-cored embankment dams. Many studies have been performed for dynamic properties of clayey soils, but they have been limited for earth core materials of dams. This study conducted resonant column tests to obtain shear modulus reduction (G/G max ) and damping ratio (D) curves for 31 specimens (17 undisturbed and 14 remolded specimens) from 13 earth-cored embankment dams. Empirical G/G max and D curves are proposed for dynamic properties of clayey earth core materials. Fitting curves are provided by using the functional forms of the Ramberg–Osgood and Darendeli models. The observation shows that the undisturbed earth cores yield relatively higher G/G max and lower D curves than the remolded cores. G/G max curves of compacted earth cores are relatively higher than those of Vucetic and Dobry curves for a similar level of plasticity index. Uncertainty and bias are calculated by performing residual analysis, which shows that there is no clear bias in predicting G/G max and the uncertainties between undisturbed earth core materials and natural deposits are at a similar level. A proposed empirical relationship of G/G max and D curves for earth core materials can be utilized for dynamic analyses of embankment dams for cases where there is insufficient in situ data.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.521

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 head0.014
GPT teacher head0.227
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 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

Citations36
Published2018
Admission routes1
Has abstractyes

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