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Record W1977848673 · doi:10.1139/l00-010

Fibre optic sensors in civil engineering structures

2000· article· en· W1977848673 on OpenAlexfundvenueaboutno aff
R. C. Tennyson, T. Coroy, Graham Duck, Gerald N. Manuelpillai, P. Mulvihill, D.J.F. Cooper, PW E Smith, Aftab A. Mufti, S J Jalali

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 CanadaDalhousie UniversityUniversity of TorontoMcMaster University
KeywordsStrain gaugeOptical fiberFiber Bragg gratingAerospaceInstrumentation (computer programming)EngineeringStructural health monitoringWireless sensor networkComputer scienceMechanical engineeringStructural engineeringTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents an overview of the development and application of ISIS fibre optic sensor (FOS) technology by the University of Toronto Institute for Aerospace Studies and Department of Electrical and Computer Engineering. The primary focus of this technology has involved the use of fibre Bragg gratings (FBGs) to measure strain and temperature in concrete structures and fibre reinforced plastic (FRP) overwraps applied to concrete structures. A brief review of existing fibre optic sensor configurations and the advantages of using FOS compared to other strain sensors is first presented. Subsequently, the development of new sensor concepts such as a long gauge of arbitrary length, a distributed gauge for measuring local strain gradients, and multiple FBGs on a single fibre optic cable are discussed, with examples of their application to civil engineering structures. In addition, the specialized instruments under development that are essential for obtaining strain information from these sensors are also described. Finally, the issue of wireless remote monitoring of FOS systems is addressed.Key words: fibre optic sensors, Bragg gratings, civil engineering structures, instrumentation.

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), Insufficient payload (model declined to judge)
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 score0.102
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.175
Teacher spread0.170 · 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

Citations46
Published2000
Admission routes3
Has abstractyes

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