Erection of solid columns of one-storey industrial buildings with bridge cranes made of high-strength sandy concrete and its economic efficiency
Bibliographic record
Abstract
The use of high-strength sandy concrete (HSSC) is an alternative to high-strength crushed stone. Its use is profitable for those regions of Russia in which crushed stone is an imported building material. Thus, crushed stone is supplied to the Republic of Tatarstan (RT) from the Ural, and the local reserves of sand are significant. Authors presented the results of studies to determine the economic efficiency of solid columns’ erection in one-story industrial buildings with bridge cranes according to the 1.424.1-5 series from HSSC of HSSC60 and HSSC80 classes in comparison with heavy concrete of B20...B80 classes. Studies have shown that in relation to Kazan, the use of HSSC of HSSC60 and HSSC80 classes in comparison with heavy concrete of B20...B40 classes, depending on the size of the span, column spacing, floor height and lifting capacity of cranes, can reduce steel consumption by 43.2…71.5 %. At the same time, the total cost of materials (steel and concrete) when using heavy concrete of B20...B40 classes is 1.7 %...38.1 % lower than with HSSC60 and HSSC80. This is due to the sharp rise in the cost of concrete in the Russian market in the third quarter of 2002 and continuing to the present (second quarter of 2021). When recalculated before the indicated price increase, the use of HSSC60 and HSSC80 in comparison with heavy concrete of B20…B40 classes gives a decrease in the total cost of materials by 1.9...34.5 %. The results obtained are novel because in the scientific and technical literature there is no information about the design of these columns from the HSSC.
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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) | 0.002 | 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".