Application of acoustic emission monitoring for assessment of bond performance of corroded reinforced concrete beams
Bibliographic record
Abstract
This article presents the results of an experimental investigation on the application of acoustic emission monitoring for the evaluation of bond behaviour of deteriorated reinforced concrete beams. Five reinforced concrete beam–anchorage specimens designed to undergo bond failure were exposed to corrosion at one of the anchorage zones by accelerated corrosion. Two additional beams without exposure to corrosion were included as reference specimens. The corroded beams were subjected to four variable periods of corrosion, leading to four levels of steel mass loss (5%, 10%, 20% and 30%). After these corrosion periods, all seven beams were tested to assess their bond performance using a four-point load setup. The beams were continuously monitored by attached acoustic emission sensors throughout the four-point load test until bond failure. The analysis of acquired acoustic emission signals from bond testing was performed to detect early stages of bond damage. Further analysis was executed on signal strength of acoustic emission signals, which used cumulative signal strength, historic index ( H( t)) and severity ( S r ) to characterize the bond degradation in all beams. This analysis allowed early identification of three stages of damage, namely, first crack, initial slip and anchorage cracking, before their visual observation, irrespective of corrosion level or sensor location. Higher corrosion levels yielded significant reduction in both bond strength and corresponding acoustic emission parameters. The results of acoustic emission parameters ( H( t) and S r ) enabled the development of a damage classification chart to identify different stages of bond deterioration.
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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 | 0.001 | 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) | 0.001 | 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".