Study of the Effect of Plastic Waste Fibers Incorporation on the Behavior of Self Compacting Concrete
Bibliographic record
Abstract
The use of waste and by-products has been for many decades for the manufacturing of building materials such as concrete and mortars. That responds simultaneously to reasons of economy of natural resources in aggregates (C&D waste) and the possibility of recovering industrial waste additions as replacement in composites. The present study focuses on the use of plastic waste fibers embedded in the cement matrix of self-compacting concrete SCC to improve its properties, either rheological mainly, the stability at fresh state or mechanical responses such as compressive and tensile strengths at hard state. This incorporation of plastic fiber reinforcement on the structure matrix could result an alternative SCC composite as a ductile material with enhanced properties. In this context, the self-compacting composites by adding a variable percentage of plastic fibers at 0.5, 1, 1.5% is formulated. Hence, the effect of plastic fibers waste on the rheological and mechanical properties of SCC is assessed. The obtained results in the present study let us to conclude the beneficial effect of such inclusion of plastic fibers on this new confected SCC fiber composition with acceptable rheological, physical and mechanical properties compared to those of a normal SCC concrete.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".