Evaluating the ASTM C944 Rotating Cutters Method for Determining the Abrasion Resistance of Concrete
Bibliographic record
Abstract
ABSTRACT Abrasion damage is a concern for some types of structures, and the need for proper quantification of it is essential. This research is intended to provide an evaluation for ASTM C944/C944M-19, Standard Test Method for Abrasion Resistance of Concrete or Mortar Surfaces by the Rotating-Cutter Method, (rotating cutters method) to assess concrete abrasion resistance. The current investigation involves analysis of the interaction between the cutters and the test specimen and the interpretation of the test results. Such details are important so that the test results could be repeatable, reproducible, and always interpreted in the same fashion. Sources that influence the test inaccuracy were studied to quantify its effects on the test results. In the first instance, the characteristics of the resulted abraded area and its effect on the depth measurement strategy were clarified. Then, the evaluation of abrasion depth using different approaches were investigated. In addition, the effect of the concrete tested surface nature (formed, finished, and cut) was examined. The examined points are believed to be the sources of test high variability. A mitigation of this variability was provided by addressing and analyzing them so it can be avoided in future evaluation. The results indicated that by correctly identifying the characteristics of the abraded patterns and by using a suitable measurement approach, less variability in the average abrasion depth was obtained. In addition, it was noted that using different depth measurement methods could lead to different abrasion depths results. Also, it was found that the tested surface characteristics could highly affect the abrasion test results.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".