Wave amplitude of embedded ultrasonic transducer-based damage monitoring of concrete due to steel bar corrosion
Bibliographic record
Abstract
In this research, four embedded ultrasonic piezoelectric transducers were combined to form cross pair and opposite pair monitoring schemes for continuously monitoring the damage to different strength grades of concrete caused by the corrosion of reinforcements under accelerated corrosion conditions. The damage process was analyzed by combining the electrochemical effects of steel corrosion, that is, half-cell potential and galvanic current tests. Results show that the embedded ultrasonic transducer method can detect damage of concrete during steel corrosion and that each stage of damage can be determined from the plots of ultrasonic transducer data versus corrosion rate. The results further indicate that a combination of cross pair and opposite pair testing methods can more comprehensively reflect the damage to concrete caused by the expansion of corrosion of steel bars, than a single testing method. Since electrochemical testing can only depict the corrosion state of steel rebars, it is beneficial to use embedded ultrasonic measurements to monitor the damage process of concrete. The differences in damage between different strength grades of concrete, that is, the resistance to corrosion of steel bars and brittle failure, can be obtained from the plots of ultrasonic transducer data.
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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 | 0.001 | 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) | 0.001 | 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".