Influence of Feedback Control on Flexural Toughness of Fiber Reinforced Concrete in ASTM C1399 Tests
Bibliographic record
Abstract
Abstract The influence of feedback control on the measured flexural toughness of fiber reinforced concrete (FRC) remains elusive. Some tests such as ASTM C1609/C1609M-07 require closed-loop control, while others such as ASTM C1399/C1399M-10 are considered control independent, and hence open-loop testing is allowed. Recent field experience has indicated that results from even ASTM C1399/C1399M-10 tests may be test control dependent. Towards this end, a test program was initiated to understand the influence of feedback control in ASTM C1399/C1399M-10 tests. Tests were performed on specimens of two different concrete strengths and one dosage of a polymeric fiber under both open-loop and closed-loop environments. In addition to performing the analysis using the ASTM C1399/C1399M-10 approach, Ri values as per the Canadian Highway Bridge Design Code (CHBDC-S06-16) were calculated. Ri values are derived from the Average Residual Strength (ARS) values obtained from ASTM C1399/C1399M-10 tests. The results indicate that while the influence of feedback control on the measured ARS values in the case of normal strength FRC is only marginal, its influence on high strength FRC is significant. The same applies to the Ri values calculated in CHBDC-S06-16, where the results indicate that based on the published minimum acceptance criteria, the choice of feedback control may in fact govern the acceptance or rejection of a given FRC material. In the context of these findings, it is recommended that beyond a certain compressive strength, ASTM C1399/C1399M-10 tests should only be performed in a closed-loop environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".