Regression and Cluster Analysis of GGBS based geopolymer composite at different proportion of Ceramic Dust
Bibliographic record
Abstract
In compare to Portland cement, geopolymer have much lower CO2 emissions, which led to growing interest in their use as an environmentally sustainable binder. The investigation of the strength in compression and durability characteristics of geopolymer composite produced at various calcined clay and ground granulated blast furnace slag (GGBS) proportions (upto 50:50) with 12M of sodium hydroxide and ratio of sodium silicate to sodium hydroxide as 2. The strength of the produced composites was evaluated after 7, 28, 56 and 90 days of ambient air curing. The durability characteristics were evaluated using Rapid Chloride Permeability Test (RCPT), acid and sulfate attack using 5% MgSO4 and 5% H2SO4 solutions respectively and for the integrity using ultrasonic pulse velocity (UPV) test. Test results showed that the developed GPC (Geopolymer composite) has several advantages over standard concrete. The strength in compression (MPa) of the SC100 (standard concrete) as compared to Geopolymer Concrete in Compressive strength was increased by replacing GGBS with calcined clay up to 10%. At 56 and 90 days, the compressive strength of 10% calcined clay samples were improved by 20.15% and 21.60% respectively as compared to the SC100. Correlations showed that strength is having a strong relationship with the chloride ion permeability and the pulse velocities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".