Experimental Investigation and Image Processing to Predict the Properties of Concrete with the Addition of Nano Silica and Rice Husk Ash
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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