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Record W3216911921 · doi:10.1520/gtj20210021

A New Apparatus for Installing Distributed Optical Sensors onto Uniaxial Compression Test Specimens to Measure Full-Field Strain Responses

2021· article· en· W3216911921 on OpenAlexafffund
S. Hegger, Nicholas Vlachopoulos, Mark S. Diederichs

Bibliographic record

VenueGeotechnical Testing Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de la Défense NationaleNuclear Waste Management Organization
KeywordsExtensometerStrain gaugeInstallationStrain (injury)Compressive strengthMaterials scienceGeotechnical engineeringStructural engineeringStiffnessCompression (physics)Composite materialEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The uniaxial compressive strength (UCS) test is a key tool used by the geotechnical industry to determine the strength and stiffness behavior of intact rock. A fundamental procedure in the process of these experiments is strain response measurement. The standard method of measuring UCS test strain response is to use discrete strain measuring devices such as extensometers or electric strain gages, or both, at the mid-height of a specimen. However, by using a novel technique of integrating distributed optical strain sensing with UCS testing, a high-density full-field strain response of UCS specimens can be measured. This article presents a novel device that can be used as a turnkey solution for installing optical strain sensors to the exterior of UCS specimens for the benefit of measuring full-field specimen strain during UCS testing.

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)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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
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

Citations1
Published2021
Admission routes2
Has abstractyes

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