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Record W2038492154 · doi:10.2113/jeeg10.3.295

Detection of Surface Breaking Cracks in Concrete Members Using Rayleigh Waves

2005· article· en· W2038492154 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.

Bibliographic record

VenueJournal of Environmental and Engineering Geophysics · 2005
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsGolder Associates (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRayleigh waveRayleigh lengthRayleigh scatteringWavelengthBeam (structure)Surface waveWavenumberGeologyOpticsLove waveSeismologyAcousticsWave propagationMechanical waveLongitudinal wavePhysics

Abstract

fetched live from OpenAlex

Abstract This study examines the use of Rayleigh waves for the detection and sizing of surface-breaking cracks in concrete members. First, finite element simulations are performed to define the conditions for Rayleigh wave propagation in members with rectangular cross-section followed by an experimental study with a concrete beam. Time histories recorded at different locations are 2D Fourier transformed into the frequency-wavenumber domain to enhance interpretation and data analysis. Rayleigh waves form at depths less than half the beam depth. With the introduction of a slot, Rayleigh waves are not observed behind the slot, except for the shortest slot depth, and the slot depth cannot be estimated in the frequency-wavenumber domain. Autospectrum calculations reveal strong Rayleigh wave reflections in front of the slot and by can be used to estimate slot depth when the wavelength is less than half the beam depth.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.402

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 head0.003
GPT teacher head0.161
Teacher spread0.158 · 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