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Record W2839137742 · doi:10.1109/i2mtc.2018.8409773

Investigation and modelling of acoustic guided waves in steel grounding rods

2018· article· en· W2839137742 on OpenAlexfundno aff
Nicholas M. Durham, Junhui Zhao, Gregory Bridges, D. J. Thomson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaManitoba HydroResearch Manitoba
KeywordsRodAcousticsTransducerElectromagnetic acoustic transducerPiezoelectricitySIGNAL (programming language)Echo (communications protocol)GroundGuided wave testingMaterials scienceEngineeringElectrical engineeringUltrasonic sensorComputer scienceUltrasonic testingPhysics

Abstract

fetched live from OpenAlex

A circuit model is presented for a piezoelectric-based, pulse-echo method for detecting the presence of corrosion in grounding rods for power transmission substations. Our pulse-echo method uses an acoustic guided wave generated from a piezoelectric transducer that is sent down the length of the grounding rod. The wave travels down the rod, reflects off the end and travels back to the piezo transducer. The wave's signature is recorded using a Data Acquisition System. The structural integrity of the rod can then be characterized based on the nature of the reflected wave. A SPICE model of the system based on Leach's model has been developed and tested. The electro-acoustic circuit model of the system can predict the overall behavior of the system. A symmetrically driven pair of steel rods was used to demonstrate that the model reproduces the main features of the signal. Defect detection was demonstrated in steel rods containing simulated defects and also in recovered grounding electrodes.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.040
GPT teacher head0.229
Teacher spread0.189 · 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 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
Published2018
Admission routes1
Has abstractyes

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