Empirical Relationships on Mechanical Properties of Class-F Fly Ash and GGBS Based Geopolymer Concrete
Bibliographic record
Abstract
Ordinary Portland Cement (OPC) is one of the important materials used in the development of the construction industry.Production of OPC consumes lot of raw materials, emits extreme quantity of carbon dioxide (CO2) into atmosphere and highly energy-intensive.Thus, to reduce the production of cement so many attempts are done and an alternative binder was found.The alternative to cement concrete is geopolymer concrete (GC) and it is produced by mixing industrial by-products (rich in aluminosilicate) with an alkaline solution.This paper explores the mechanical properties and a representative non-linear equation was proposed for the relationship between splitting tensile Vs compressive strengths and flexural Vs compressive strengths of fly ash-GGBS synthesized GC cured at ambient temperature for 28 days.This work also presents empirical formulae for predicting elastic modulus of fly ash-GGBS synthesized GC.Equations were proposed to determine the splitting tensile strength, flexural strength and elastic modulus based on the compressive strength of GC.The obtained results are clearly indicating that the predicted (from proposed equations) splitting tensile and flexural strength values are very close to the experimental values.It is established that the formulae found in various codes and literature generally predict the higher values of the elastic modulus than those obtained using the proposed equation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".