Optimizing Physical and Mechanical Properties of Recycled Filler and Fiber Sand Concrete: A Full Factorial Design Approach
Bibliographic record
Abstract
In response to the escalating economic, technical, and environmental challenges associated with the disposal of waste from construction, demolition, and agricultural sectors, this study investigates the formulation of an innovative sand concrete.This concrete incorporates recovered materials, specifically recycled fines from waste concrete as fillers and date palm waste as fibers, to address the urgent need for sustainable construction materials.Utilizing a 2 3 full factorial experimental design, this investigation rigorously examined the impact of three critical parameters: fiber percentage (FP), recycled filler percentage (RFP), and fiber length (FL), on the physico-mechanical properties of the resulting sand concrete.The analysis, conducted with the statistical software JMP Trial 16, revealed divergent effects of these variables on the material's properties.It was observed that while both fiber length (FL) and fiber content (FP) exert a significant influence on the concrete's characteristics, the proportion of recycled fillers (RFP) integrated into the mixture displayed a negligible effect.Notably, the incorporation of recycled fillers and fibers into sand concrete significantly enhanced its flexural strength.Comparisons with control sand concrete demonstrated a substantial increase in strength, with improvements of up to 19.5%.This exploration not only contributes to the body of knowledge on sustainable building materials but also underscores the potential of utilizing agricultural and construction waste to enhance the performance of concrete.The findings suggest that strategic incorporation of waste-derived fibers and fillers could play a pivotal role in the development of stronger, more environmentally friendly construction materials.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".