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Record W2910246595 · doi:10.2316/j.2019.206-5582

STUDY ON RUST DETECTION OF RC STRUCTURE BASED ON ELECTROMAGNETIC PULSED EDDY CURRENT

2018· article· en· W2910246595 on OpenAlexvenueno aff
Jia Jia, Jun Cheng, Yuanheng Zhang, Senhua Zhang, Xiaogang Li

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2018
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
FundersNational Science Fund for Distinguished Young ScholarsNational Key Research and Development Program of China
KeywordsCurrent (fluid)Eddy currentRust (programming language)Electrical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

A set of rust detecting system for reinforced concrete was designed based on the electromagnetic pulsed eddy current (EPEC). The EPEC detecting test on the reinforced concrete specimens with different reinforcement diameters was performed. The finite element (FE) model was established. The results of the reinforcement position experiment show the Hall voltage peak value (HVPV) of the same lift-off value positively correlates with the reinforcement's diameter, and the HVPV declines fast with the increase of the lift-off value. The rust experiments results show that with the same liftoff value, the HVPV increases with the increase of the horizontal distance from the Hall sensor to the reinforcement in the specimen.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.359

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 spread0.261 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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