Use of acoustic emission signals through steel reinforcement to detect the onset of corrosion.
Bibliographic record
Abstract
Corrosion processes produce elastic energy waves in the form of acoustic emission (AE). For corrosion in reinforced concrete structures, AE waves are emitted beginning with the depassivation of oxide layers of reinforcing steel, the initial stage of corrosion processes. This study examines the feasibility of using AE technique to detect corrosion through steel reinforcement, which has relatively lower attenuation than concrete. Comparison of coupling AE sensors on steel and on concrete was made. Accelerated corrosion regime and two-channel multifunctional AE equipment with piezoelectric sensors were employed for laboratory experiments. The detectable distance from corrosion source to sensor was estimated via the calculation of attenuation coefficient and particle surface displacement. The analysis of source location demonstrates that surface wave is the predominant AE wave propagating in rebars with one-inch diameter. Furthermore, an important experiment was performed to compare AE measurement with half-cell potential measurement. As a result, AE was proved to be a promising tool for corrosion detection in reinforced concrete structures.Dept. of Civil and Environmental Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C443. Source: Masters Abstracts International, Volume: 44-03, page: 1435. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".