MétaCan
Menu
Back to cohort
Record W3086325923 · doi:10.1139/cgj-2020-0145

New fiber Bragg grating (FBG)-based device for measuring small and large radial strains in triaxial apparatus

2020· article· en· W3086325923 on OpenAlexvenueno aff
Wen-Bo Chen, Wei-Qiang Feng, Jian‐Hua Yin, Jie-Qiong Qin

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsFiber Bragg gratingMaterials scienceOpticsSIGNAL (programming language)Strain gaugeCompassAcousticsDeformation (meteorology)Accuracy and precisionOptical fiberStructural engineeringComposite materialPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

In triaxial tests, it is of great importance to accurately measure the radial strain of a specimen. However, devices for radial strain measurement are relatively fewer than the ones for vertical strain measurement. In this study, a new measurement device based on the fiber Bragg grating (FBG) was developed to determine the small and large radial strains of a soil specimen. The new device includes two yokes and two compass-type mechanisms, which are mounted on diametrically opposite sides of the specimen. Metal strips bonded with FBG sensors were chosen as sensing elements and installed at two ends of two legs of a compass-type mechanism so that the small deformation of the specimen could be amplified by a constant ratio to a more detectable value with a higher signal–noise ratio. Considering the temperature sensitivity of FBG and the real working environment, the new device was carefully calibrated underwater. Validation tests on an intact triaxial specimen demonstrated that the new device can work reliably and accurately under both static and cyclic loadings.

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: none
Teacher disagreement score0.706
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 head0.035
GPT teacher head0.229
Teacher spread0.194 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicAdvanced Fiber Optic SensorsFrench-language works237,207