STUDY OF PHYSICAL AND MECHANICAL CHARACTERISTICS OF VARIOUS TYPES OF SOIL STRENGTHENED BY CLINKER WASTE
Bibliographic record
Abstract
the specificity of the construction of roads is the use of a huge amount of various stone materials - sand, gravel, etc. To reduce the cost of road pavements, it is possible to replace stone materials with local soils. Such materials do not need to be transported by road or rail. It is known that most soils cannot be used in the construction of the road bed (sandy loam and loam are light dusty and heavy dusty, clay dusty and oily), as their use leads to the appearance of various deformations of the upper layers of road pavement. This problem can be solved by strengthening the soil with various types of binders, in particular, clinker waste. When conducting experimental studies to assess the effectiveness of the use of waste clinker production to strengthen the soil the following compositions without using: soil and 9% waste; soil and 18% waste; soil and 27% waste were investigated. As a result of the research, the main dependences of the changes in the physical and mechanical parameters of the soil concrete on the amount of waste clinker production introduced into the composition of various types of soils were identified. It was found that the use of waste clinker production to strengthen the loam allowed to obtain a reinforced soil brand M20. Strengthening sand with clinker production without the addition of cement is not practical, as the samples do not gain even minimal strength for 180 days, which allows them to be classified by brand, in addition, such samples have extremely low resistance.
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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 | 0.001 |
| 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".