Statistical modeling of mechanical and transport properties of concrete incorporating glass powder
Bibliographic record
Abstract
The objective of this study is to model the effect of the partial replacement of cement by glass powder (GP), w/cm, and supplementary cementitious materials (SCM) content, as well as their coupled effects on key engineering properties of concrete using a statistical design of experiments. The modeled experimental domain includes concrete mixtures with w/cm ranging between 0.27 and 0.69, GP percentages of 0–50%, and SCM content of 310 to 440 kg/m3. The modeled responses include the compressive strength and rapid chloride ions permeability (CIP) at various ages. The comparison between predicted and measured responses determined on eight selected mixtures included in the experimental domain indicates good accuracy of the established models to describe the effect of the independent variables on the targeted properties. The derived statistical models indicate that the CIP is dominated by substitution percentage of GP, while the compressive strength is dominated by w/cm, regardless of the age of concrete. The increase in GP content to 30% resulted in a significant reduction in CIP. However, it reduces the compressive strength at early age, which may necessitate a decrease in w/cm to compensate for strength reduction. Trade-off between mixture parameters to achieve targeted compressive strength and CIP properties were established.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".