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Record W1997192822 · doi:10.1139/l00-006

Distributed strain sensing for structural monitoring applications

2000· article· en· W1997192822 on OpenAlexfundvenueaboutno aff
Michael D. DeMerchant, Anthony W. Brown, Jeff Smith, Xiaoyi Bao, Theodore W. Bremner

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural health monitoringBrillouin scatteringStrain (injury)Optical fiberDistributed acoustic sensingBrillouin zoneImage resolutionKey (lock)Computer scienceOpticsFiber optic sensorMaterials scienceRemote sensingEngineeringStructural engineeringPhysicsGeology

Abstract

fetched live from OpenAlex

Strain sensors are a valuable tool for assessing the health of structures. The University of New Brunswick, in conjunction with ISIS Canada, is developing a distributed fibre optic strain sensor based on Brillouin scattering. This sensor can provide a virtually unlimited number of measurement points using a single optical fibre. A description of the operating principles of the system is given, along with a summary of laboratory test results. Strain measurement accuracy as high as approximately ±11 µε has been demonstrated at 1 m spatial resolution. Spatial resolutions as short as 100 mm can be used, although with decreased strain measurement accuracy. Future development of the technology will include an enhancement allowing both strain and temperature to be measured simultaneously.Key words: strain sensor, fibre optics, distributed sensing, structural monitoring, Brillouin scattering.

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 categoriesnone
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 score0.772
Threshold uncertainty score0.730

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 integrity0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations11
Published2000
Admission routes3
Has abstractyes

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