Statistical Modelling and Prediction of Compressive Strength of Concrete
Why this work is in the frame
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Bibliographic record
Abstract
The matrix mixture of concrete can be made to have high compressive strength. In the present paper, statistical model was built-up to predict the compressive strength of concrete containing different matrix mixtures at fixed age or at different age of 1, 3, 7, 28, 56, 90 and 180 days. The model examines eight different parameters for the matrix mixture that includes: time, water, cement, metakaolin (MK), silica fume (SF), sand (S), aggregate (A) and superplasticizer (SP). This research addresses the effect of the matrix mixture of concrete on the compressive strength, where this information will help the cement industry in producing the required concrete strength. The results from the predicted model have high correlation to the experimental results for the concrete compressive strength.
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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) | 0.012 | 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