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Record W3130917775 · doi:10.13168/agg.2021.0005

Shear modulus and damping ratio of clay soil under repeated freeze-thaw cycles

2021· article· en· W3130917775 on OpenAlexaff
Mahya Roustaei

Bibliographic record

VenueActa Geodynamica et Geomaterialia · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Alberta
FundersIslamic Azad University
KeywordsDamping ratioOverburden pressureMaterials scienceShear modulusConsolidation (business)Geotechnical engineeringDynamic modulusComposite materialModulusDynamic mechanical analysisGeology

Abstract

fetched live from OpenAlex

The dynamic properties of soil deposits subjected to dynamic loading, such as the shear modulus G and material damping ratio D, are important parameters in ground response seismic analysis. In seasonally frozen regions, these properties can be significantly affected by microstructural changes that occur during freeze-thaw cycles. The current study evaluated the dynamic properties of clayey soil exposed to freeze-thaw cycles. Dynamic triaxial testing was conducted to determine the influence of mean effective consolidation stress, cyclic stress ratio, loading frequency, and freeze-thaw cycles on G-γ and D-γ curves. Scanning electron microscopy (SEM) was carried out to investigate microstructural changes in the clay soil fabric. The results indicate the freeze-thaw process has an important effect on dynamic properties of the soil. The dynamic shear modulus increases with increasing effective confining pressure, loading frequency, and confining pressure, and decreases with increasing number of freeze-thaw cycles. Development of more voids between clay particles after ice lens formation during these cycles results in an increase in the damping ratio, but this trend decreases with increasing confining pressure. Increasing the loading frequency increases or decreases the damping ratio depending upon the mean effective confining pressure and number of freeze-thaw cycles.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.993

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)0.0090.000

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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 designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueActa Geodynamica et GeomaterialiaSame topicClimate change and permafrostFrench-language works237,207