Acoustic Emission Waves Propagation in Rubberized Concrete under Special Monitoring Conditions
Bibliographic record
Abstract
This study investigates the change in the acoustic emission (AE) parameters emitted in rubberized concrete under abrasion action at a sub-freezing temperature (−20°C). Seven concrete mixtures were developed with two coarse-to-fine aggregate ratios (C/F) (2.0 and 0.7), various crumb rubber (CR) content (0%, 10%, 20%, and 30%), and different rubber particle sizes [4.5 mm CR and 0.4 mm powder rubber (PR)]. Rotating cutter tests were conducted on three 100 mm cubic samples from each mixture at −20°C and 25°C while monitored via an AE system. AE parameters such as amplitude, number of hits, and signal strength were collected and underwent two parameter-based analyses: b-value and intensity analysis approaches, resulting in three additional parameters: b-value, severity (Sr), and the historic index [H(t)]. Results showed that testing concrete samples under abrasion at cold temperature,−20°C, resulted in a decrease in the emitted number of hits, cumulative signal strength (CSS), Sr, H(t), and an increase in b-values compared to testing at 25°C. Furthermore, incorporating rubber particles was found to decrease the AE signals’ amplitudes significantly at 25°C and slightly at −20°C, which manifested the higher wave attenuation occurrence at ambient temperature compared to cold temperature. AE analysis also showed a decrease in the abrasion resistance for mixtures with higher C/F, higher CR content, and larger rubber particle size. These decreases were more noticeable at 25°C compared to −20°C. Finally, the study developed two damage classification charts to estimate the ranges of abrasion mass loss percentage and wear depth in terms of the intensity analysis parameters: Sr and H(t).
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".