Strength and Elastic Properties of Low-Fine Self-Compacting Concretes Designed with Nano SiO2
Bibliographic record
Abstract
Self-compacting concrete (SCC) is increasingly takes place in construction applications due to its excellent selfcompacting characteristic.It is known that, in order to achieve self-compacting feature, it is necessary to use high volume of fine materials in the mix design with effective superplasticizers.Using high volume of fine materials causes a decrease in the amount of total coarse aggregates.Coarse aggregates have an important role on the strength and elastic properties of concretes.This paper presents the results of an experimental study which investigated the strength and elastic properties of SCC mixtures modified with nano-SiO2.Nano-SiO2, having 35nm average particle size, was used with the aim of reducing the total fine material in SCC designs.Five different nano-SiO2 percentages (0.5%, 1.0%, 1.5%, 2.0% and 2.5%) were utilized and the total fly ash content used in the reference mixture was gradually reduced.The volumetric emptiness occurred by the fly ash reduction was filled by aggregates.Successful mixtures which exhibited desired SCC properties were subjected to compressive tests at 28 th and 120 th days.Moduli of elasticity of concrete specimens were also measured.Results have shown that with increasing W/Cm ratio, compressive strengths were decreased.The use of nano-SiO2 in reduced-fly ash content mixtures could not compensate the strength decrement.However, the use of nano-SiO2 combined with fly ash has prominently enhanced the elastic modulus of nano-modified SCC mixtures.
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".