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Record W4320917547 · doi:10.3390/s23042194

Distributed Impact Wave Detection in Steel I-Beam with a Weak Fiber Bragg Gratings Array

2023· article· en· W4320917547 on OpenAlexafffund
Yuan Wang, Neil A. Hoult, Joshua E. Woods, Hannah Kassenaar, Xiaoyi Bao

Bibliographic record

VenueSensors · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsQueen's UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsTransport Canada
KeywordsOpticsFiber Bragg gratingDistributed acoustic sensingMaterials scienceStructural health monitoringAcousticsBeam (structure)Rayleigh scatteringHarmonicCantileverVibrationOptical fiberRayleigh lengthFiber optic sensorPhysicsLaserLaser beams

Abstract

fetched live from OpenAlex

In this paper, acoustic, dynamic and static strain variations along a steel I-beam generated by an impact load are reconstructed simultaneously within a single measurement. Based on the chirped pulse φ-OTDR system with the single-shot measurement technique, both a higher strain-sensing resolution and a higher measurable vibration frequency are achieved. In addition, a weak fiber Bragg gratings array (WFBGA) with enhanced Rayleigh reflection is employed as a sensor, providing high signal-to-noise ratio Rayleigh traces, resulting in lower measurement uncertainty. In the experiments, the damping constant and fundamental frequency of the damped harmonic oscillator could then be measured based on the recovered strain variation profile for further structural health analysis. Compared with commercial strain gauges, linear potentiometers, and OFDR systems, the proposed sensing system ensures a distributed, quantitative, and high-frequency sensing ability, with an extensive range of potential applications.

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: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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