Toughness Characterization of Fiber-Reinforced Concrete: Which Standard to Use?
Bibliographic record
Abstract
Abstract The major advantage of fiber-reinforced concrete (FRC) over its plain counterpart is in its improved energy absorption capability, or ‘toughness.’ There are currently several standard test methods available to characterize the toughness of fiber-reinforced concrete, but little is known of the relationship between the toughness results they produce for a given fiber-reinforced concrete. An attempt is made here to compare the results produced by three of these techniques: ASTM C 1018, ASTM C 1399, and JSCE SF-4 for the same concrete and to assess the subjectivity encountered in toughness characterization. It was found that there is no firm and reliable correlation between these three procedures; they would rank different FRCs differently. Only a weak correlation exists between the toughness parameters generated by the C 1399 and the SF-4 standards, and the correlation is highly dependent on the fiber type. The ASTM C 1018 procedure is the least reliable of all and produces Toughness Indices and RM,N values that are very difficult to interpret.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Comparison of concrete toughness test standards; measurement standards in materials engineering, and the 'reproducibility' language is assay-level, a polysemy trap.
This compares concrete testing standards and measurement results, not research methods as a social or scholarly practice.
Compares engineering materials-test standards for concrete toughness; object is construction materials testing, not research.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".