Characterisation of damage due to abrasion in SCC by acoustic emission analysis
Bibliographic record
Abstract
This investigation evaluates and compares the abrasion resistance of various concrete types by means of acoustic emission (AE) analysis. Normal concrete, self-consolidating concrete (SCC) and SCC with variable supplementary cementing materials (SCMs) were tested under the rotating cutter method for abrasion resistance. The effect of using different SCMs in SCC mixtures including fly ash, metakaolin (MK), silica fume and slag on the abrasion resistance of SCC was examined. In conjunction with the abrasion testing, AE monitoring was simultaneously conducted on all mixtures using AE attached sensors. AE parameters such as signal amplitude, signal strength, number of hits, duration and cumulative signal strength (CSS) were collected during the abrasion tests. Three additional parameters were determined through further analyses: b-value, severity (Sr) and historic index (H(t)). Results from the abrasion tests indicated that the SCC mixture containing MK had the highest abrasion resistance among all tested mixtures. The studied AE parameters including CSS, number of hits, b-value, H(t) and Sr were well correlated to the extent of abrasion damage in all tested specimens. The progression of abrasion damage was associated with increased AE activities indicated by high fluctuations in the b-value and H(t) along with ever-increasing values of CSS, number of hits and Sr. The AE intensity analysis quantified which ranges for H(t) and Sr would indicate the extent and severity of the damage due to abrasion by means of developed damage classification charts.
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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".