Thermal Properties of Light Weight Self - Compacting Concrete Incorporate Nano Silica
Bibliographic record
Abstract
In Iraq, certain rocks called porcelanite can be used to make lightweight concrete.The objective of this paper is to create lightweight self-compacting concrete (SCC) by utilizing 0.4 w/cm, coarse and fine porcelanite aggregate and adding nano silica to these mixtures.This study looks at a different way of doing things.Instead of using sand in concrete, they used varying amounts of fine porcelanite instead.We replaced 10%, 20%, 30%, 40%, and 50% of the sand with porcelanite to see how it would influence the concrete's thermal characteristics.Further examined the ratio of water to cement, using ratios of 0.4 and 0.5 by doing this, we wanted to see how both ratio of water to cement, and amount of porcelanite affected the thermal properties of the concrete.It was found that porcelanite makes self-compacting concrete harder to work with, and less able to conduct and spread heat.However, it does make the concrete hold more heat.When nano silica was used, it reduced workability of SCC.However, its effect on thermal properties was not of much significance.
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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".