Optimizing Mortar Strength for Infrastructure Applications Using Rice Husk Ash and Municipal Solid Waste Incineration Ash
Why this work is in the frame
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Bibliographic record
Abstract
Infrastructure development increasingly requires sustainable construction materials, with waste utilization serving as a key strategy to address this need. Employing eco-friendly materials with enhanced engineering properties not only mitigates the environmental impact of waste but also lowers the carbon footprint associated with cement production. Accordingly, this research aims to investigate the potential of enhancing the performance of municipal solid waste incineration ash (MSWIA) mortar through the incorporation of rice husk ash (RHA) as a supplementary cementitious material (SCM), thereby supporting the principles of a circular economy. The MSWIA mortar comprised 25% bottom ash (BA) and 5% fly ash (FA) as substitutes for fine aggregate and cement, respectively. Cement was then replaced with RHA at 5–30% to assess the influence of RHA on the properties of MSWIA mortars such as workability, strength development, and water absorption. Adding RHA led to a lower flow rate and setting time than mortar content-only MSWIA. Nonetheless, the various mechanical properties of MSWIA mortar, such as compressive strength, split tensile strength, and flexure strength, were found to be increased when the RHA quantity was used at 10% as a cement replacement. The water absorption of the mortar mixes was reduced by increasing RHA up to 15%. The test results revealed that the mortar’s microstructural properties were notably enhanced, and the UPV measurements confirmed the overall good quality of the mortar specimens. Therefore, incorporating RHA and MSWIA in construction not only enhances performance but also contributes to environmental sustainability by reducing the carbon dioxide emission and landfill waste.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it