Developing a comprehensive prediction model for the compressive strength of slag-based alkali-activated concrete
Bibliographic record
Abstract
This study aims to evaluate the effects of mix design parameters of ambient-cured slag-based alkali-activated concrete (GAAC) and develop a prediction model for its compressive strength (CS) by emphasizing the chemical compositions of alkaline solutions. A test setup including 625 specimens, in 125 mixes, was designed. A comprehensive parametric study and statistical evaluation were performed. Findings revealed the effectiveness of Na2O, SiO2, H2O, and GGBFS contents compared to the dosage of alkaline solutions and highlighted their disadvantages. The results also discovered the efficiency of the Bayesian linear regression in the simulation compared to the artificial neural network. Two models for estimating the CS of GAAC with reasonable accuracy were also proposed. Carbon footprint evaluation revealed that the carbon dioxide reduction of substituting ordinary concrete with GAAC depended on the desired properties of the concrete and was equal to 33% for grade 35 MPa concrete.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".