Experimental Investigation and Image Processing to Predict the Properties of Concrete with the Addition of Nano Silica and Rice Husk Ash
Bibliographic record
Abstract
The use of the combination of ultrafine rice husk ash (RHA) and nano silica (NS) enhances the compactness of hardened concrete, but there is still a lack of studies that address the effects of NS and RHA on the workability, mechanical properties and pore microstructure of concrete. This study mainly aims to investigate the influence of the pore size distribution in multiphysics concrete model modified by NS and RHA and to determine the workability and mechanical properties of concrete with NS and RHA. In this work, NS and RHA were used as 0, 5, 10, 15 and 20% replacements of ordinary Portland cement (OPC) in concrete grade M20. Concrete mixed with NS and RHA showed improved performance for up to 10% addition of NS and RHA. Further addition of NS and RHA showed a decrease in performance at 7, 14 and 28 days. The decrease in concrete porosity was also found to be up to 10% when adding NS and RHA to cement. Image processing was performed on the cement-based materials to describe the microstructure of the targeted material without damage. The results from the experimental and tomography images were utilized to investigate the concrete microstructure and predict its inner properties.
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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 | 0.000 | 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".