Effect of GGBS and Nano Silica on the Durability Properties of Ternary Concrete
Bibliographic record
Abstract
The cement industry is the most energy consuming and CO2 producing industry.Many efforts has been taken to reducing the cement content in construction industry using supplementary cementitious materials(SCM).Aim of this research work is using supplementary cementitious materials(SCMs),such as Ground Granulated Blast furnace Slag(GGBS) and Nano-Silica(Average particle size-15nm) combinations with Ordinary Portland Cement(OPC).The Nano-Sillica is partially replace with cement at dosages of 1%,2%,3%,4%,5% and also 50% Ground Granulated Blast furnace Slag(GGBS) to find effect of durability properties of High strength concrete(HSC).Replace with cement content for significant improvements in performance were we observed that Nano-Silica gives better durability compare to 100% Ordinary Portland Cement(OPC) controlled concrete.Durability assessments such as rapid chloride permeability test(RCPT)(at ages of 28,56 and 90, 180 days), Sorptivity test( at age of 90 days) , sulfate attack test( at age of 56,90 and 180 days), Ultrasonic pulse velocity(UPV)test(at age of 28, 56, 90 and 180 days) and Acid attack test( at age of 90 days) is revealed significant resistance against chloride penetration ,water absorption, sulfate chemical also check.These improvements can mainly due to larger specific area of Nano-Silica, which effectively stimulates both pozzolanic reactivity and filler effect over the cementitious matrix.Due to its specific properties ,Nano-silica may constitute the significant improvement of the quality of the concrete structure.Application of nanotechnology is an effective way to reduce environment pollution and improve durability of concrete.Nano silica replace with cement at dosages of 4% gives the optimum results of the durability 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 | 0.000 |
| 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) | 0.001 | 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".