Shear behavior of fiber-reinforced lightweight concrete beams reinforced with BFRP bars
Bibliographic record
Abstract
This paper presents an experimental investigation on the shear performance of fiber-reinforced lightweight concrete (FRLWC) beams reinforced with basalt fibers reinforced polymer (BFRP) bars. Ten beams 150x250x2400 mm were tested under four-point loading configuration until failure. The investigated parameters included the concrete type (steel fiber-reinforced lightweight concrete (SFRLWC), basalt fiber-reinforced lightweight concrete (BFRLWC), and polypropylene fiber-reinforced lightweight concrete (PFRLWC)), the fibers’ volume fraction (0, 0.5, and 1.0 %), and the beams’ longitudinal reinforcement ratios (0.95 and 1.37 %). The test results demonstrated that beams cast with SFRLWC containing 0.5 % steel fibers achieved shear capacities 23 % and 16 % higher than those cast with BFRLWC and PFRLWC, respectively. Similarly, beams cast with SFRLWC containing 1 % steel fibers showed greater increase in shear capacity, with gains of 47 % and 41 % compared to BFRLWC and PFRLWC, respectively. BFRLWC beams demonstrated intermediate performance between SFRLWC and PFRLWC beams, with reduced crack width than PFRLWC beams. Increasing the reinforcement ratio from 0.95 % to 1.37 % showed a shear capacity increase of 16 % for beams cast with BFRLWC. A new model was proposed to predict the shear capacities of FRWLC beams reinforced with BFRP longitudinal bars. The proposed model accurately predicted the shear capacities of BFRLWC beams with predicted-to-experimental ratio of 1.01 (standard deviation, SD = 0.05). It also predicted the shear capacities of PFRLWC and SFRLWC beams with V pred /V exp = 0.99 (SD = 0.06) and 0.90 (SD = 0), respectively.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".