EXPERIMENTAL STUDY ON CORROSION MONITORING AND ASSESSMENT OF CONCRETE STRUCTURES USING ACOUSTIC EMISSION
Bibliographic record
Abstract
This paper aims to employ acoustic emission (AE) technique to detect and identify the level of damage resulting from reinforcing steel corrosion in reinforced concrete structures. Five reinforced concrete prism samples with a constant concrete cover (20 mm) were subjected to an accelerated corrosion test. The samples were corroded until reaching five different levels of theoretical mass loss: 1, 2, 3, 4 and 5% calculated using Faraday’s law. The corrosion activity of the five samples was continuously monitored using attached AE sensors and a data acquisition system. The acquired AE signal parameters including AE signal strength, counts, energy, and number of hits were analyzed firstly to detect corrosion onset and secondly to predict the extent of damage as a result of corrosion propagation. The results obtained from AE monitoring were analyzed, evaluated, and compared to half-cell potential (HCP) measurements, and the amount of current passing with time. The analysis of these results involved comparing the AE cumulative signal strength (CSS) and each of the actual/theoretical percentages of mass loss, passed electrical current, and HCP readings. This analysis showed that CSS can be correlated to both HCP and electrical current at all degrees of damage from corrosion initiation up to 5% of mass loss. In addition, other AE signal parameters including number of hits, counts, and energy showed a significant increase in their cumulative values as the level of the theoretical mass loss, actual mass loss, and crack widths were increased. The results of this investigation confirmed that AE parameters (especially CSS) can be evaluated to detect corrosion earlier than other available nondestructive testing and evaluation techniques. Furthermore, it can be employed to represent the extent of damage occurring in reinforced concrete structures caused by corrosion of reinforcing steel.
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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 | 0.000 | 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".