Analysis of the Effect of Using Nano Silica Mixture on the Compressive Strength of Lightweight Concrete
Bibliographic record
Abstract
The development of urban areas is so rapid, especially in the field of construction projects such as building construction. The development of urban areas is followed by the development of increasingly sophisticated technologies, especially in this concrete technology. This results in an increase in the concrete mixture. However, the numerous demands for the strength of high quality concrete motivate the writer to investigate by using nano silica mixtures as added ingredients. In this thesis, the writers use a mixture of K-300 concrete with nano silica which is adjusted to the levels that have been calculated with the aim to determine changes in the slump value, compressive strength and bonding time of the concrete. In this study, a mixture of fresh concrete with K-300 quality was mixed with nano silica with levels of 3%, 5% and 7% to cement and normal concrete with 0% nano silica as a control. The writers carry out the process of compressive strength testing of concrete in accordance with the age of concrete that has been determined that is 7, 14, 21, and 28 days with slump values about ± 12 . At the age of 28 days the results obtained were compressive strength of 208,940 kg/cm2, 216,560 kg/cm2 and 240,770 kg/cm2 with nano silica content of 3%, 5% and 7%, which experienced a decrease in compressive strength of concrete compared to the results obtained in compressive strength of normal concrete was 265,68 kg/cm2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".