Mechanical performance of M40 Grade concrete with partial replacement of GGBFS and Silica Fume
Bibliographic record
Abstract
Concrete is an important building material used in the construction of different types of civil engineering structures. The production of cement, an integral part of concrete releases large amounts of carbon dioxide, green house gases that exacerbates climate change. In order to reduce the environmental impact of cement production, supplementary materials can be used. The addition of supplementary cementitious materials to concrete improves the overall properties of concrete through pozzolanic activity. This study is designed to evaluate the feasibility of using Ground granulated blast furnace slagand Silica fume as substitute for cement in concrete. It can be reduced the cost of concrete and the rate of cement consumption. In our research, we investigate the strength properties of concrete using certain percentage of GGBFS and Silica fume with the replacement of cement. The cement was replaced by 20%, 30% and 35% GGBFS and 5%, 10% and 15% Silica fume respectively. The w/c ratio, fine aggregates and coarse aggregates were kept as per the design mixes. M40 grade concrete is used in the experiment. The specimens were prepared. The concrete was tested for fresh properties such as workability and mechanical properties like compressive strength for 7 days, 14 days and 28 days respectively. The test results conclude that the combined addition of GGBFS and silica fume as substitute of cement at various amounts gives positive effect on workability and strength.
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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".