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Record W2889030684 · doi:10.1139/cgj-2018-0153

Bender elements in stiff cemented clay: shear wave velocity (<i>V</i><sub>s</sub>) correction by applying wavelength considerations

2018· article· en· W2889030684 on OpenAlexvenueno aff
Qasim Khan, Sathya Subramanian, Dawn Yun-Cheng Wong, Taeseo Ku

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersMinistry of Education, India
KeywordsShear (geology)RepeatabilityGeotechnical engineeringWavelengthStiffnessSine waveShear wavesGeologyWave velocityMathematicsEngineeringStructural engineeringOpticsPhysicsStatistics

Abstract

fetched live from OpenAlex

For the quality control of cement mixing in clays, small-strain shear stiffness G max is now increasingly being used due to enhanced repeatability in shear wave velocity (V s ) measurements. These stiff cemented clays have higher resonant frequencies that require the use of higher input frequencies in bender element testing for reliable V s measurements. However, the practical requirements for suitable signals (with minimal near-field effects and wave reflections) can often be difficult to implement. To facilitate such V s measurements, the current study proposes a methodology that can correct V s values corresponding to lower wave propagation distance to wavelength ratios (L tt /λ) to more reliable values of V s at reference L tt /λ criterion suggested in previous studies (e.g., 2, 3.33, and 4). Two clay types are mixed with ordinary Portland cement and various mix ratios are utilized to cover a wider range of soil stiffnesses. Based on the collected database, it is found that the resulting fitting functions enable the reasonable estimation of the stabilized V s values corresponding to the suggested L tt /λ criterion regardless of the nature of the input sine signal.

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 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 score0.441
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

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