Innovative use of brick wastes as coarse aggregate in concrete
Bibliographic record
Abstract
Abstract Coarse aggregates occupy the largest volume in concrete which is one of the most widely used construction material as per industry surveys. The depleting supply of coarse aggregate coupled with the high greenhouse gasses emissions from its processing and transportation has resulted in a need to find alternativesthat can be utilized as coarse aggregate. Of such materials that are available in abundance locally in Pakistan are brick wastes that are generated from the construction and demolition processes. In order to evaluate the feasibility of using brick wastes as coarse aggregate in producing concrete, this study was undertaken. Six concrete mixtures were made by incorporating brick wastes as a replacement for the natural coarse aggregates and the corresponding properties evaluated. The properties evaluated are the slump, density, compressive strength and flexural strength. Results from this study indicated that the use of brick wastes as coarse aggregates in concrete resulted in a decrease in the slump and mechanical properties. However, concrete mixtures incorporating brick wastes up to 100% replacement of natural coarse aggregate exhibited flexural and compressive strength higher than 2 MPa and 10 MPa respectively. Nonetheless, the incorporation of brick wastes as coarse aggregate resulted in a decrease in density due to its lower bulk density in comparison to that of the natural coarse aggregate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".