Influence of Fiber Type on the Performance of Reinforced Concrete Beams Made of Waste Aggregates: Experimental, Numerical, and Cost Analyses
Bibliographic record
Abstract
The structural performance of concrete structures requires swelling the bending and shear characteristics of reinforced concrete (RC) beams. The bending characteristics of RC beams consisting of waste granite aggregate (WGA), steel fibers (SF), polypropylene fibers (PF), and glass fibers (GF) are assessed in this research. Twenty-one 2,000 mm×200 mm×250 mm RC beams were cast and tested. WGA was sorted and utilized instead of natural coarse aggregates (NA), with three mass replacement fractions: 0%, 50%, and 100%. Besides, SF, PF, and GF were utilized separately at three fractions of 0%, 0.5%, and 1%. Beams were loaded under a four-point bending arrangement, and the ultimate bending resistance, deformability, stiffness and crack development were recorded and assessed. Also, an evaluation of experimental results and existing design standards in terms of maximum crack width has been carried out. Moreover, a cost-sensitivity examination has been made to analyze the effectiveness of using various fibers in terms of cost. Experiments revealed that the impact of PF on enhancing the load-bearing capability of beams with WGA was greater than that of strengthened with SF and GF. However, the impact of GF on the ultimate deformability of WGA RC beam samples was superior to that of PF and SF. PF has a greater influence on enhancing the flexural capacity of RC beams than SF; nevertheless, SF has a greater influence on deformation. The ductility and deformability of RC beams were substantially enhanced when GF was introduced in specimens made with WGA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".