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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 OpenAlexafffund
A. Zerwer, Maria Anna Polak, J. Carlos Santamarina

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations46
Published2005
Admission routes2
Has abstractyes

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