Developing SonReb models to predict the compressive strength of concrete using different percentage of recycled brick aggregate
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
This study was conducted to determine the relationship between nondestructive and destructive tests in concrete cubes using different ratios of normal stone and recycled brick as coarse aggregates. Variations in the grade of concrete, density, and age were considered to make the model prediction more efficient in places where the use of recycled brick aggregates is common. Normal concrete grades M20, M25, and M30 were considered having density variation by replacing stone with recycled brick aggregate, and age by testing concrete strength at 7, 28, and 84 days. A regression model was created using artificial neural networks and multiple regression analyses. The study showed that the regression model developed using an artificial neural network predicted better results. The models obtained from the experiment were compared with other models provided by different authors. The study also considered the effect of using recycled brick aggregate in nondestructive tests and the modulus of concrete.
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) | 0.001 | 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