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Record W2051509194 · doi:10.1088/0964-1726/10/3/320

Structural health monitoring of innovative bridges in Canada with fiber optic sensors

2001· article· en· W2051509194 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSmart Materials and Structures · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Manitoba
FundersMinistère des TransportsNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsStructural health monitoringBridge (graph theory)Fiber Bragg gratingOptical fiberFiber optic sensorReliability (semiconductor)Structural engineeringMeasure (data warehouse)EngineeringFiberComputer scienceMaterials scienceTelecommunicationsComposite materialPhysics

Abstract

fetched live from OpenAlex

This paper describes the development and application of fiber optic sensors for monitoring bridge structures. Fiber Bragg gratings (FBGs) have been used to measure static and dynamic loads on bridge decks and columns, including composite repairs for rehabilitation purposes. A new long gage concept that permits overall average strains to be measured has also been developed with gage lengths varying from 1-20 m. These gages can be bonded to the concrete structure or imbedded in the composite repair patch. Six projects undertaken by ISIS Canada to incorporate fiber optic sensing to monitor the structural health of bridges in Canada are described. Data will be presented for several bridges that indicate a measure of system reliability over several years in a hostile environment. The benefits of fiber optic sensors will be highlighted.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.950

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 head0.223
Teacher spread0.213 · 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