Feasibility of Using Waste Glass Sludge in Production of Ecofriendly Clay Bricks
Bibliographic record
Abstract
Burnt clay bricks are commonly used in construction across the globe. The objective of this study is to explore the potential of using waste glass sludge (WGS) as a secondary material in clay brick manufacturing. WGS was collected during industrial-scale cutting and polishing of glass. Brick specimens were manufactured using various dosages (i.e., 5, 10, 15, 20, and 25% by clay weight) of WGS at an industrial brick kiln plant. A range of mechanical and durability tests were performed on the bricks thus produced to quantify their performance. Clay bricks incorporating WGS exhibited higher compressive and flexural strength as compared with that of control traditional clay bricks. The unit weight of bricks was reduced owing to WGS addition, which can lead to lighter and economical structures. Furthermore, the resistance against efflorescence, sulfate attack, and freeze-thaw was enhanced for all the clay bricks incorporating WGS. Scanning electron microscopy indicated a well-bonded and fused structure of brick specimens incorporating WGS. The findings demonstrate that WGS can enhance the physical and mechanical properties of clay bricks, leading toward more economical and sustainable construction.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".