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Record W4392963747 · doi:10.1520/gtj20230363

Development of Tools for the Direct Measurement of Shear Stress and Shear Strain within a Soil Mass

2024· article· en· W4392963747 on OpenAlexaff
Mark Talesnick, Ichinur Omer, M. Ringel, Mason Ghafghazi

Bibliographic record

VenueGeotechnical Testing Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeotechnical engineeringDirect shear testShear (geology)Shear stressGeologyStress–strain curveMaterials scienceDeformation (meteorology)Composite materialPetrology

Abstract

fetched live from OpenAlex

Abstract This article describes the development of sensing tools designed and applied to the direct measurement of shear stresses and shear strain within a soil mass. The importance in the development of these tools is in their ability to measure shear stresses and shear distortions within soil without any a priori information of the stress and strains applied at the boundaries of the system. These tools may be applied to element testing, testing of physical models, and field applications. The sensors were used in the testing of a sand in a large simple shear apparatus, under both constant height and constant vertical pressure conditions. Testing demonstrated that shear stresses smaller than 0.1 kPa are easily resolved. Shear strains smaller than 10−5 (abs) were reliably measured. Measurement of shear stress and shear distortion within the soil mass allows for direct determination of the shear stiffness of the soil. Shear stiffness measured in this fashion is significantly greater than that determined from the globally measured shear stress and shear strain. The stiffness degradation curve illustrates a constant shear stiffness over the range of 10−5 through 5·10−4.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.240
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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