High-strength concretes based on anthropogenic raw materials for earthquake resistant high-rise construction
Bibliographic record
Abstract
This work is devoted to development of optimum recipes of high-strength concretes based on filled binders with fine-milled anthropogenic mineral filler intended for earthquake resistant high-rise monolithic construction. The optimum recipes of concretes in this work have been developed on the basis of computations and experimental designing of cast concrete mixes with chemical additives and anthropogenic mineral fillers, as well as destructive inspection methods as the most precise for analysis of physicomechanical and deformation properties of concrete. The following raw materials have been used for production of high-strength concretes: natural quartz sands with the fineness modulus F.M. = 1.7-1.8; crushed limestone with the particles sizes of 5-20 mm; water reducing chemical additives and hardening retarder to control specifications of concrete mixes; plain Portland cement, grade PTs 500 D0; anthropogenic mineral additives (fillers) in the form of crushed concrete and ceramic bricks. Optimum recipes of monolithic concretes have been designed using anthropogenic raw materials including normal concrete grades with compressive strength of M30-M40 and high-strength concrete grades of M50-M80, characterized by high homogeneity of cement stone with significantly finer pores and lower shrinkage. Herewith, it has been established that fine-milled anthropogenic mineral filler in the form of crushed concrete and ceramic bricks at the ratio of 70:30, respectively, efficiently influences specifications of concrete mixes on their basis significantly increasing resistance of the mix against sedimentation and water gain. It has been established that the developed high-strength concretes based on filled binders with fine-milled anthropogenic mineral filler are characterized by high freeze–thaw resistance (from F400 to F600) and water tightness (W14 and higher), which is a solid base providing high lifecycle of such concretes.
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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 | 0.001 | 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".