Statistical interpretation of strength data for High-volume fly ash concrete – a new mix proportioning technique
Why this work is in the frame
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Bibliographic record
Abstract
Posing its importance in building infrastructure, compressive strength is regarded as one of the most critical parameters to choose the grade of concrete. High performance concrete production not only involves huge physical effort and quality control, but also involves the incorporation of supplementary cementitious material in conjunction with high quality additives. In this present study, high volume fly ash content is used for high performance concrete mix. For any mix proportioning technique adopted, regression analysis is of utmost importance to validate the model. This current work covers detailed literature study on various forms of regression models developed for fly ash based concrete mixes. A new mix proportioning technique is adopted with cement replacement with class F fly ash as well partial addition of sand with fly ash as fine aggregate. In this present study, a linear regression analysis is conducted which produced excellent correlation coefficient for prediction of compressive strength at 28-days curing for high-volume fly ash concrete mixes. For the pulverized fuel ash substitution from 40%, 50%, 60% and 70% (cem:F ash 1:1), improved workability properties were obtained with increased chemical admixture content (~1-2%). Good correlation coefficient (R 2 ) values of 0.9831 or 98.3% are obtained at higher w/b ratios for 28-days curing. Trendline indicates the linear relationship with R 2 value of 0.9831 for higher w/b ratios and 0.974 for lower w/b ratios, suggesting a strong negative correlation. This signifies, a better model fit. Higher R 2 values of 0.98 signifies, good model fit, while R 2 values of 0.843 shows 15.7% variance due other properties in concrete such as reduced pozzolanic activity and fly ash reactivity at higher fly ash content beyond 40% and low water-binder ratios. Lower standard deviation values show that the data set is closely tight.
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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 | 0.001 |
| 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