Mathematical Modeling for Evaluating the Mechanical Properties of High Strength Concrete with Natural Zeolite and Additives
Bibliographic record
Abstract
The cement manufacturing industry is a major contributor to atmospheric pollution, primarily due to carbon dioxide emissions.Consequently, there is a pressing need to develop eco-friendly concrete capable of mitigating air pollution by sequestering atmospheric carbon dioxide.In this context, the incorporation of Natural Zeolite in concrete has been investigated, as it can absorb environmental carbon dioxide.This study explored the effects of partial cement replacement with Natural Zeolite (5%) and varying percentages of Silica Fume, Metakaolin, and Fly Ash (5%, 10%, and 15%) on the mechanical properties and carbon sequestration potential of High Strength Concrete (HSC).Comprehensive testing was conducted to evaluate the split tensile, compressive, and flexural strengths of the modified HSC.Experimental results indicated that the addition of Natural Zeolite and Metakaolin enhanced the strength of HSC, with Mix 3 displaying a higher 90-day compressive strength compared to the reference mix.The findings suggest that incorporating Natural Zeolite and other supplementary cementitious materials in concrete has the potential to alleviate environmental pollution.The dataset, comprising 900 samples, exhibited no autocorrelation or multicollinearity issues, making it suitable for multiple regression analysis.The statistically significant regression models developed in this study can effectively predict concrete strength.
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.
How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".