The Effect of the Use of Residues (Metallic Shavings) in the Formulation of Self-Compacting Concrete in Fresh and Hardened State
Bibliographic record
Abstract
Self-Compacting Concretes (SCCs) have brought promising insight into the concrete industry to generate environmental impact and reduce costs. the absence of SCC vibrations generates a significant reduction in noise pollution in urban areas, ease of implementation, possibility of concreting heavily reinforced areas or areas with complex geometry and obtaining better quality concrete on the one hand, reduction of working time on site, of personnel during installation and reduction of the costs of industrial processes, on the other hand. Metal chips resulting from the modification of metal parts obtained by turning present problems of environmental pollution and storage. The recycling of this waste in the construction industry is an adequate solution for the production of concrete and can improve some of its properties. An experimental study to study the properties of SCC containing metal shavings with the study of the properties of SCC in the fresh state: flow, L-box and sieve stability. Properties in the hardened state of concretes: compressive strength and ultrasonic pulse velocity. The metal waste in the form of shavings, incorporated in a dosage of 0.5% of concrete volume in the mixtures of SCCs produced, which makes it possible to evaluate the effect of the addition of metal shavings on the characteristics of SCCs in the state fresh and hardened and to recover metal waste, the results confirm the advantage of adding metal shavings in the fresh state the SCCs studied keep their characteristics of self-plasticity, in the hardened state the results show an improvement in the compressive strength of the SCCs studied.
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".