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Record W2073878950 · doi:10.1139/l10-080

Strain monitoring in a reinforced concrete slab sustaining service loads by distributed Brillouin fibre optic sensors

2010· article· en· W2073878950 on OpenAlexafffundvenue
Beatriz Martín‐Pérez, Amre Deif, Bruno Cousin, Chunshu Zhang, Xiaoyi Bao, Wenhai Li

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Ottawa
FundersNetworks of Centres of Excellence of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSlabMaterials scienceStrain gaugeCrackingStructural engineeringBrillouin zoneBendingService lifeDurabilityUltimate tensile strengthOptical fiberDuctility (Earth science)Composite materialComputer scienceOpticsEngineeringCreep

Abstract

fetched live from OpenAlex

Reinforced concrete (RC) structures deteriorate and as a result crack due to extreme loading and (or) environmental conditions. Damage accumulation as such adversely affects the structure’s durability properties, impairing its service life. The intensity of cracking in an RC structure is usually regarded as the key criterion toward damage assessment and repair intervention. This paper presents the results of an experimental program in which the concrete strains of a small-scale RC slab sustaining in-service loads were monitored by traditional electrical strain gauges and distributed Brillouin fibre optic sensors in an attempt to detect damage due to crack formation. A comparison of these measurements with classical bending theory is also presented. The results show that distributed Brillouin fibre optic sensors can capture both tensile and compressive strains in concrete; however, the accuracy of their measurements is dependent on the proper installation of the sensors and the fibre length over which the strains are to be measured.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
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 integrity0.0000.002
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations15
Published2010
Admission routes3
Has abstractyes

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