Usage Possibilities of Diatomite in the Concrete Production for Agricultural Buildings
Bibliographic record
Abstract
Construction materials evidently affect economy, strength, durability, safety and expediency of constructions. Selecting locally available material will bring a cost-advantage to structures built in rural parts. Such a case is especially valid for agricultural structures. In present study, effects of a natural pozzolan, diatomite admixture on concrete workability characteristics, setting duration and behavior under axial loading were investigated and possible use of diatomite-blended concrete as a light-weight construction material in agricultural structures was evaluated. This research was carried out in Tokat/Turkey in 2012. Concrete samples were prepared by using different admixture ratios of diatomite as a light-weight aggregate with standard sand and crashed sand aggregates. Water/cement ratios of mixtures were determined by taking a constant slump value into consideration. Unit weight, compressive strength and water absorption test were carried out over the samples. According to the results, unit weight, compressive strengths and water absorption in 150, 200 and 250 doses changed with increasing diatomite contents, respectively, from 1470 kg/m3 to 2210 kg/m3, from 20.45 MPa to 1.14 MPa, from 6.04% to 23.85%. Results revealed significant cost-savings by using diatomite aggregate to produce light-weight concrete blocks to be used in agricultural structures. It was also concluded that such blocks might provide significant insulative advantages for heat-balance of livestock barns..
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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".