Enhancing Concrete Properties through Supplementary Cementitious Materials and Predictive Modeling
Bibliographic record
Abstract
The growing demand for high-performance and sustainable concrete necessitates the incorporation of supplementary cementitious materials (SCM) into the concrete matrix to reduce cement consumption and mitigate the associated carbon footprint, a substantial contributor to greenhouse gas emissions. The proposed research explores the utilization of different industrial and agricultural byproducts including fly ash, silica fume, metakaolin, and rice husk ash, as cementing material to improve performance and sustainability. These materials exhibit higher pozzolanic behavior by reducing the porosity and contribute to the strengthening of the Interfacial Transition Zone leading to improved strength. Mechanical properties such as compressive, flexural, and tensile strength are obtained. Machine learning (ML) techniques reduce the need for extensive experimental trials and streamlines the process. The proposed research adopts a Graph Neural Network (GNN) model that analyzes experimental data, gets trained with the laboratory results and predicts the mechanical properties of concrete and identifies key factors influencing concrete performance. Testing results indicate that the GNN model exhibits higher R 2 values and lesser statistical error values when compared to the other existing models in the literature. This clearly implies that advanced ML models like GNN can be utilized in feasible, efficient and rapid prediction of the strength properties of 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".