Potential of Coal Bottom Ash as Fine Aggregates in The Production of Lightweight Brick
Bibliographic record
Abstract
The use of coal bottom ash, a by-product from the coal burning process in lightweight bricks production is getting more attention worldwide including in Malaysia. The main objective of this research is to assess the extended potential of bottom ash to produce lightweight bricks. The green technology-based lightweight bricks in this study are designed using the by-product resulting from the burning of pulverized coal in thermal power plants for electric power generations. The burning of pulverized coal was questioned extensively over the last decades due to the carbon dioxide emission which is about 2.0 billion tons annually. These lightweight bricks are produced from the combination of specifically graded bottom ash, classified pozzolanic ashes, fine aggregates, Ordinary Portland Cement, and water. The lightweight bricks were subjected to a few laboratory assessments including compressive strength test, density test, and water absorption test. It was observed that the C2 brick and C3 brick at 60% and 100% addition of bottom ash, respectively had comparable compressive strength with other commercialized bricks (cement-sand brick and clay brick). The addition of coal bottom ash also produces the lowest density of the bricks which is about 1.2 kg/m3. Comparison with other commercialized bricks available in the market in terms of compressive strength, density, and water absorption indicated that the bottom ash-based bricks are stronger and lighter. A comprehensive selection of good quality materials and the selected mix design produced an acceptable quality of bricks that can be considered as part of the actual construction materials. Thus, with the production of this light and strong brick, the volume of the coal ash waste that was dumped in the landfill will be substantially reduced and simultaneously the lighter bricks will reduce the dead loads supported by the main structures of the building.
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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 | 0.000 |
| 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".