Magnetic Data Pattern Features at Longitudinal Defect Sites in Rebars Scanned by a Passive Magnetic Inspection Technology
Bibliographic record
Abstract
ABSTRACT Reinforced concrete is a versatile modern construction material. Despite its advantages as a composite material, corrosion of the embedded reinforcing steel leads to infrastructure deterioration and loss of service. Non-Destructive Testing (NDT) methods are required to quantify the reinforcement condition, and to help manage human and financial risks arising from unexpected outright failure or service restrictions. Reinforcement condition can be assessed using a novel, time- and cost-efficient NDT method based on the self-magnetic behaviour of ferromagnetic materials. In this study, the magnetic properties of three similar rebars, each having three similar sized longitudinal defects, are recorded and assessed through experiments and a numerical simulation model. Strong correspondence is demonstrated between the magnetic properties from numerical simulation and from the experimental objects. For instance, applying the experimentally obtained defect detection threshold to the mathematically simulated results allows accurate defect detection in the simulations, showing that self-magnetic behavior is a powerful tool for condition assessment of ferromagnetic reinforcing materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".