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Record W2006257601 · doi:10.1139/l06-131

Distributed Brillouin sensor for structural health monitoring

2007· article· en· W2006257601 on OpenAlexfundvenueno aff
Fabien Ravet, Lufan Zou, Xiaoyi Bao, Togay Ozbakkaloglu, Murat Saatçioğlu, Joe Zhou

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural health monitoringStructural engineeringBucklingColumn (typography)Pipeline (software)Deformation (meteorology)Fibre-reinforced plasticMaterials scienceRodBrillouin zoneComputer scienceEngineeringComposite materialMechanical engineeringOptics

Abstract

fetched live from OpenAlex

The distributed Brillouin sensor (DBS) was used to monitor the structural changes in a steel pipe and a composite column subjected to heavy loads. The column was made of concrete reinforced with fibre-reinforced-polymer (FRP) rods and sheets. The test reproduced earthquake-like conditions. The pipe had a length of 2.58 m and diameter of 0.75 m. The DBS measured the strain distribution in both the concrete column and the pipe under various loads. The DBS provided detailed information on the structure's health at the local and global level, before any deformation, cracks, or buckling was visible. This work demonstrates that the DBS is capable of extracting critical information useful to engineers: the engineer's experience and judgement, in conjunction with appropriate data-processing methods, make it possible to anticipate structural failures. The DBS is a promising tool for structural health monitoring.Key words: structural health monitoring, distributed Brillouin sensor, concrete structure, pipeline buckling, strain measurement.

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 score0.666
Threshold uncertainty score0.881

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 head0.010
GPT teacher head0.229
Teacher spread0.218 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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