USE OF IRAQ CONCRETE SCRAP AS FILLER AND AGGREGATE OF HEAVYWEIGHT AND LIGHTWEIGHT CONCRETE
Bibliographic record
Abstract
the relevance of the paper is due to the search for alternative sources of raw materials for the construction industry, associated with the disposal of man-made waste. The novelty of the article is to identify the scien-tific laws of the influence of demolition waste on buildings and structures on the formation of the micro-structure of lightweight and heavyweight concrete. Concrete waste was prepared as both fillers of cement materials and fine aggregates, based on which concrete with high mechanical properties was created. The mix design was carried out from the point of view of geomimetics, in particular, taking into account the law of affinity of structures. The strength characteristics of concrete mixtures were investigated in accordance with EN 12390-3. In addition, the microstructural, morphological and thermal properties of the raw materials and concrete were determined during 28-day curing. For the first time, the dense microstructure of the com-posite was ensured, both with Portland cement products and with hydration, and, in part, with hydration products of previously unreacted clinker, whose minerals are present in concrete waste and are activated when they are crushed. The use of demolition waste of buildings and structures as a filler of cementing ma-terial when replacing Portland cement up to 20% allows to obtain better compressive strength of both heav-yweight and lightweight concrete.
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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".