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Record W2063124001 · doi:10.1117/12.388132

New generation of Fabry-Perot fiber optic sensors for monitoring of structures

2000· article· en· W2063124001 on OpenAlexaff
Pierre Choquet, François Juneau, John Bessette

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsRoctest (Canada)
Fundersnot available
KeywordsFabry–Pérot interferometerOptical fiberFiber optic sensorMaterials scienceInterferometryOpticsStrain gaugeOptoelectronicsStructural health monitoringPhysics

Abstract

fetched live from OpenAlex

This paper reports on a new generation of Fabry-Perot fiber optic sensors to be used in parallel or in replacement of conventional instruments for monitoring of structures. The new generation of sensors is based on a unique fiber optic strain sensor that represents a breakthrough in fiber optic sensing. The novel technique is based on extrinsic Fabry-Perot white- light interferometry which offers outstanding accuracy and repeatability. Furthermore, all sensors are completely immune to lightning surcharges which opens new possibilities in the field of reliable long term structure monitoring. Instruments such as piezometers, embedment strain gages, surface strain gages, temperature sensors and displacement transducers are all available in Fabry-Perot fiber optic version. Furthermore, all these instruments use a common multimode optical fiber to carry the signal to the readout units and multi-channels dataloggers. Both static and dynamic measurements are possible with this technology. The paper presents results of laboratory and field studies on fiber optic sensors mentioned above including integrated fiber optic sensors in carbon and glass fiber reinforced polymer. A case study of bridge strain monitoring with Fabry-Perot sensors is also reported.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 designBench or experimental
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

Citations25
Published2000
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Optic SensorsFrench-language works237,207