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Record W3206548250 · doi:10.1139/cjce-2020-0658

Ultrasonic and acoustic pulse velocity methods for nondestructive detection of early decay in wood poles

2021· article· en· W3206548250 on OpenAlexaffvenue
Fernando Tallavó, Mahesh D. Pandey, Giovanni Cascante, Cristobal Lara

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOrthotropic materialNondestructive testingAttenuationAcousticsUltrasonic sensorUltrasonic testingEngineeringStructural engineeringPhysicsOpticsFinite element method

Abstract

fetched live from OpenAlex

Wood poles are widely used to support overhead distribution and transmission power lines in North America and the world. These poles are vulnerable to internal deterioration due to extreme weather conditions, requiring a large number of poles to be inspected every year. This paper presents a comparative study of four stress wave-based nondestructive testing (NDT) methods commonly used for condition assessment of wood poles. These include the traditional approaches of sounding, sonic pulse velocity, and ultrasonic pulse velocity tests; and a new approach that considers the orthotropic characteristics of wood, uncertainties in the elastic properties, ultrasonic wave velocity and attenuation. Two poles with an internal hole of 4% of the cross-section are evaluated and compared by each method. Ultrasonic measurements of wave velocity and attenuation considering orthotropic characteristics of wood and uncertainties in the elastic properties provide a reliable wave-based NDT method for the detection of early decay in wood poles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.999

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 head0.012
GPT teacher head0.273
Teacher spread0.260 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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