MétaCan
Menu
Back to cohort
Record W1974721440 · doi:10.1520/gtj100552

Characterization of Glyben for Seismic Applications

2007· article· en· W1974721440 on OpenAlexaff
M. H. T. Rayhani, M. Hesham El Naggar

Bibliographic record

VenueGeotechnical Testing Journal · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologyGeotechnical engineeringCharacterization (materials science)SeismologyMaterials science

Abstract

fetched live from OpenAlex

Abstract Glyben is artificial clay that is prepared by mixing sodium bentonite powder and glycerin. It is used for laboratory tests and scale modeling for geotechnical applications. The mechanical properties of glyben depend on the bentonite and glycerin mix proportions. The shear strength, dynamic shear modulus, damping ratio, and Poisson’s ratio were evaluated for glyben samples prepared with different glycerin/bentonite ratios. Vane shear tests, T-bar tests, hammer tests, and resonant column tests were conducted on glyben specimens and the shear strength and dynamic properties were evaluated considering a wide range of strain values and confining pressure. The measured glyben properties were compared with properties of natural cohesive soils to verify the range of applicability of glyben as a test bed material. It was found that glyben has the same range of strength and dynamic properties as soft to medium stiff clay. The trend of variation of the shear modulus and damping ratios of glyben is similar to that of natural clays. It is noted, however, that the damping ratio of glyben is higher than that of natural clays for shear strains below 0.01%. It was concluded that glyben can reasonably model the nonlinear behavior of natural soil under strong dynamic excitation.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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 designOther design
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

Citations12
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueGeotechnical Testing JournalSame topicSeismic Imaging and Inversion TechniquesFrench-language works237,207