EXPERIMENTAL STUDY ON DETECTION OF REBAR CORROSION IN CONCRETE BASED ON METAL MAGNETIC MEMORY
Bibliographic record
Abstract
This paper presents a non-destructive testing method for detection and analysis of rebar corrosion in concrete based on metal magnetic memory.A three-dimensional (3D) automatic scanning and measuring system, composed of a 3D scanning and measuring device and an automation software, was self-designed and developed.Two corroded reinforced concrete specimens were prepared by the accelerated galvanic corrosion test.A series of experiments were implemented by the proposed 3D device.The experimental processes involve acquiring the spatial position and corresponding 3D magnetic flux density near the specimens, judging the rebar's corroded region, and assessing the corrosion degree of the specimens.The results indicate that the curves of tangential magnetic field, obtained by the Y -scanning, all intersect near the edge of the steel corrosion zone, and the corrosion region can be qualitatively determined by the position and distance of the two intersecting points; the curves of tangential magnetic field, obtained by the Z-scanning, have an extreme value varying with the lift-off heights (LFHs) of the magnetic sensor in the corroded region, and the corrosion degree can be semi-quantitatively assessed by the LFH of the reversal point.The findings of this work propose an effective non-destructive detection method for rebar corrosion in concrete.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".