Developing a comprehensive prediction model for the compressive strength of slag-based alkali-activated concrete
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 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.
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 | 0.001 | 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