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Record W3129036666 · doi:10.2749/vancouver.2017.3084

Geometric effects on Ultrasonic Pulse Velocity Method for Structural Assessment – Experimental Study on Mortar Specimens

2017· article· en· W3129036666 on OpenAlexaff
Piotr Wiciak, Giovanni Cascante, M. A. P. Maria Anna Polak

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsUltrasonic sensorMortarAcousticsTransducerMaterials scienceUltrasonic testingLaser Doppler vibrometerNondestructive testingStructural engineeringEngineeringComposite materialOpticsLaserPhysicsLaser beams

Abstract

fetched live from OpenAlex

Non-destructive ultrasonic evaluation (NDE) is commonly used for assessment of civil infrastructures and characterization of construction materials. Among the acoustic methods impact echo, ultrasonic pulse velocity (UPV), and surface waves analysis can be distinguished. The UPV method, as it is an ASTM standard test method for concrete specimens, is investigated in the article. The standard specifies the applications of UPV as: assessment of relative quality of concrete, presence of imperfections (i.e. voids, cracks, and the effectiveness of its repairs). UPV can be also applied to monitoring changes in the condition of a specimen. In spite of an easiness of the method the obtained results highly depend on the transducers used, the coupling quality, and the specimen dimensions. In this article the authors focus on the sensor and the dimensions effects. The results for UPV tests on 5 mortar specimens of different heights and diameters are presented. The specimens are tested with 54 kHz and 850 kHz resonant frequency (fc) transducers and the state-of- the-art laser vibrometer (response measurements).

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.400
Teacher spread0.366 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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