Preliminary Study on the Use of Recycled Glass, Ceramics, and Granite as Fillers in Ultra-High Performance Fiber Concrete
Bibliographic record
Abstract
Waste recovery and environmental protection and the production of low-cost UHPFC are the most trends areas of current researchers.Ultrafine (silica fume) is the basic component of ultra-high-performance fiber concrete (UHPFC), which is very sophisticated and its manufacture is limited by the capacity of some industries.This work, which falls within this context, aims to recover waste (glass, ceramics, and granite) as recycled fillers partially substituted silica fumes in the composition of the UHPFC.The study consists of two parts, the first is to find a formulation based on local materials, the second is to replace the silica fumes by recycled fillers with the percentage of 10% and 20%.The tests carried out on the fresh state mixtures are density, workability and occluded air, and on hardened state are the compressive and flexural tensile strength, sclerometer, ultrasound regarding untreated concrete, and the performance in compressive and tensile strength concerning the concrete heat treated at 90℃, as well as durability parameters (water absorption and chlorides penetrations).The results show that recycled ceramic fillers and 20% of the recycled granite fillers present an acceptable compressive strength, the recycled fillers increase the flexural tensile strength of the UHPFC and the heat treatment improves the mechanical strength.Recycled fillers affect sustainability parameters.
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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 | 0.000 |
| 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".