STUDY ON RUST DETECTION OF RC STRUCTURE BASED ON ELECTROMAGNETIC PULSED EDDY CURRENT
Bibliographic record
Abstract
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.When the Hall sensor is at the same position, the HVPV increases with the increase of the diameter of the reinforcement in the concrete.The Hall voltage (HV), the HVPV, and the time of pulse rising (TOPR) of the same specimen are greater in the rust-free point than in the rusted points.And the results of the numerical simulation verify the validity of the conclusions of the physical experiments.Our above study shows that the detecting method can locate the reinforcement in the concrete accurately and ascertain its diameter effectively.Furthermore, the detecting method can identify the rust region of the reinforcement swiftly and evaluate the rust degree rapidly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".