Mechanical and Thermal Characteristics of Concrete Reinforced with Crushed Glass and Glass Fiber: An Experimental Study
Bibliographic record
Abstract
The incorporation of waste glass and glass fiber, as replacements for fine aggregate and cement respectively, offers a sustainable strategy to mitigate landfilling and virgin aggregate extraction. The present study delves into the influence of these waste derivatives on the mechanical and thermal attributes of concrete. Fine aggregate was substituted by crushed glass shards in a weight-to-weight ratio ranging from 5% to 30%, and cement was replaced by glass fiber in a weight-to-weight ratio of 1%. Mechanical attributes such as compressive, flexural, and splitting strength were evaluated, along with thermal characteristics of the concrete. A concrete mix ratio of 1:2:4 and a water/cement ratio of 0.45 were employed. The results revealed that concrete designated as Mcf20% demonstrated superior mechanical properties compared to the reference concrete. After 28 days, compressive strength of 51.2 MPa, flexural strength of 6.5 MPa, and splitting tensile strength of 3.78 MPa were recorded for Mcf20% concrete, signifying the beneficial effects of the combined use of glass fiber and crushed glass. Furthermore, an inverse relationship was observed between the percentage of waste additives and thermal conductivity. This investigation underscores the potential of recycling glass and glass fiber as eco-friendly additives in concrete, improving both mechanical properties and thermal performance, thus endorsing their use in structural and architectural concrete applications.
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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".