Comparison of different approaches for determining the residual post-cracking strength index of fiber reinforced concrete for bridges
Bibliographic record
Abstract
In Canada, there are different approaches to evaluate the performance of fiber reinforced concrete (FRC) for bridges based on the residual post-cracking strength index (Ri). They involve different combinations of test methods ASTM C78, ASTM C1399, and ASTM C1609. The aims of this study were to assess all possible (existing and new) methods to determine the Ri values, and capture the major differences between the current Canadian Highway Bridge Design Code (CHBDC S6-14) and the City of Winnipeg specification’s approaches, as a case study. Flexural tests (ASTM C78, ASTM C1399, and ASTM C1609) were performed on 60 FRC beams (100 mm × 100 mm × 350 mm) prepared from concrete provided by four ready-mix concrete suppliers according to City of Winnipeg’s bridge deck specifications for a project built in Winnipeg. The results showed that all the methods implemented herein for calculating the Ri of FRC gave comparable results. However, by using Method V, all required parameters (first peak load and residual loads at specified deflections) could be directly extracted from one load–deflection curve obtained from ASTM C1609. In addition, when using this method, the Ri can be calculated for each specimen, which enables quantifying the magnitude of variation from average values. Since this approach also requires fewer number of specimens, reducing time and cost of testing, it has been adopted by the City of Winnipeg in bridge specifications for FRC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".