Influence of Nanomaterials on the Performance of Lightweight Concrete Containing Silica Fume Against Chemical Attacks
Bibliographic record
Abstract
This research studied the influence of nano silica (NS) on the performance and properties of lightweight concrete (LWC) containing silica fume (SF) and exposed to sulfate attack (sodium and magnesium) at a concentration of 0.3%.Although previous studies have examined the effect of adding NS to ordinary concrete, its impact on LWC with SF and exposed to double sulfate attack remains limited; therefore, this study prepared various mixtures were created using two types of cement: ordinary Portland cement (OPC) and sulfate-resistant cement (SRPC), with and without the incorporation of NS, to study the changes in weight, visual inspections, and compressive strength.LWC was produced by replacing 50% of the coarse aggregate with lightweight pumice aggregate.The results showed that the highest weight loss after exposure to sulfate solution appeared in samples containing OPC without the addition of NS, at 16.2% and 24.5% after 28 and 90 days.This is due to the high tricalcium aluminate C3A content.As for the samples containing SRPC, they showed a lower loss of 6% and 8% after 28 and 90 days, which confirms the effect of the low content of C3A.The addition of 4% NS reduced the strength loss in both mixtures when exposed to chemical attack.Therefore, NS is considered a material that improves the resistance of concrete to chemical influences by decreasing the Ca(OH)₂ content.
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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 | 0.001 |
| 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) | 0.002 | 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".