Detecting Defects in Steel Reinforcement Using the Passive Magnetic Inspection Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Defects in steel reinforcement are critical factors in the evaluation of the service life of reinforced concrete (RC). Steel reinforcement (bar) defects or deterioration may lead to crack propagation and strength decrease in RC structural members. Deterioration also changes the steel magnetic properties; the evaluation of these changes can be investigated by an indirect passive and non-invasive method to locate and quantify defect in RC structures. Herein, a passive magnetic inspection (PMI) method is modified and used to examine its potential as a non-destructive testing (NDT) method for condition assessment of steel reinforcement. The passive magnetic flux density of steel bar with three small holes in three different positions and locations along the bar is measured in the laboratory. A signal processing methodology based on frequency spectrum analysis and filtering is applied to the experimental data, and the results are compared with the numerical simulation. The processed data from the experimental test shows the potential of PMI method to detect, locate and evaluate bar condition. Both experimental results (after signal processing) and simulation results show a good similarity for top and bottom holes. Cross-correlation of numerical simulation with experimental data was necessary to reveal detection of the side hole.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it