Impact Resistance Enhancement of Sustainable Geopolymer Composites Using High Volume Tile Ceramic Wastes
Bibliographic record
Abstract
The need for sustainable concrete with low carbon dioxide emissions and exceptional performance has recently increased in the building industry. Many distinct types of industrial byproducts and ecologically safe wastes have shown promise as ingredients for this kind of concrete. Meanwhile, as industrialization and lifestyle modernization continue to rise, ceramic waste becomes an increasingly serious threat to the natural environment. It is well known that free cement binder that incorporates tile ceramic wastes (TCWs) can significantly improve the material’s sustainability. We used this information to create a variety of geopolymer mortars by mixing TCWs with varied proportions of ground blast furnace slag (GBFS) and fly ash (FA). Analytical techniques were used to evaluate the mechanical properties and impact resistance (IR) of each designed mixture. TCWs were substituted for binders at percentages between 50 and 70 percent, and the resultant mixes were strong enough for real-world usage. Evidence suggests that the IR and ductility of the proposed mortars might be greatly improved by the addition of TCWs to a geopolymer matrix. It was found that there is a trend for both initial and failure impact energy to increase with increasing TCWs and FA content in the matrix. The results show that the raising of TCWs from 0% to 50, 60 and 70% significantly led to an increase in the failure impact energy from 397.3 J to 456.8, 496.6 and 595.9 J, respectively.
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".