AIR CONTENT OF SELF-CONSOLIDATING CONCRETE AND ITS MORTAR PHASE INCLUDING RICE HUSK ASH / ORO KIEKIS SAVITANKIAME BETONE IR JO SKIEDINIO DALYJE SU RYŽIŲ LUKŠTŲ PELENAIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the air content results of self-consolidating concrete (SCC) and its mortar phase including rice husk ash (RHA) as a supplementary cementing material. Moreover, this paper demonstrates a simple technique to determine the dosage of air-entraining admixture (AEA) required for the target air content in SCC. Different SCC mixtures were designed based on the water/binder (W/B) ratios of 0.30–0.50 and design air content of 4–8%. RHA was incorporated in the concretes substituting 0–30% of cement by weight. The mortars were formulated from the mixture proportions of the corresponding parent concretes and tested to determine the air content at various AEA dosages. The effects of W/B ratio and RHA content on the air content of both mortar and concrete were observed. The effect of mortar volume on the air content of concrete was also noticed. Besides, the AEA dosages required for the target air contents of concrete were estimated based on the equivalent mortar air contents. Later the air-entrained SCC mixtures were produced using AEA and tested for the air content. Test results exhibit that the air contents of both mortar and concrete were significantly influenced by the W/B ratio and RHA content. The concrete air content was also greatly influenced by its mortar volume. The AEA dosage increased with lower W/B ratio, higher RHA content, and greater mortar volume for the target air contents. In addition, the actual AEA dosages were consistent with the estimated AEA dosages of the concretes. An excellent correlation was observed between the actual and estimated AEA dosages. The strong correlation suggests that the AEA dosage needed for a target air content in concrete can be determined based on the equivalent air content of its mortar phase. Santrauka Straipsnyje pateikiami oro kiekio nustatymo savitankiame betone (SCC) ir jo skiedinio dalyje su ryžių lukštų pelenais (RHA), naudojamais kaip papildoma cementavimo medžiaga, rezultatai. Be to, pateikiamas paprastas būdas parinkti orą įsiurbiančio priedo (AEA) dozę, siekiant gauti reikalingą oro kiekį SCC. Suprojektuoti skirtingi SCC mišiniai su skirtingu vandens ir rišiklio (W/B) 0,30–0,50 santykiu ir numatytu 4–8 proc. oro kiekiu, RHA buvo dedamas į betoną pakeičiant 0–30 proc. cemento pagal masę. Skiediniai buvo formuojami pagal jiems artimo betono sudėtis ir oro kiekis juose bandomas su skirtingomis AEA dozėmis. Nustatyta W/B santykio ir RHA kiekio įtaka oro kiekiui tiek skiedinyje, tiek betone bei skiedinio tūrio įtaka oro kiekiui betone. Be to, AEA dozės, reikalingos numatytam oro kiekiui betone pasiekti, nustatytos pagal ekvivalentinį oro kiekį skiedinyje. Vėliau SCC mišiniai su orą įsiurbiančiu priedu buvo pagaminti naudojant AEA ir išbandytas juose esantis oro kiekis. Tyrimų rezultatai rodo, kad tiek skiedinį, tiek betono oro kiekį labai veikia W/B santykis ir RHA kiekis. Oro kiekį betone taip pat smarkiai veikia skiedinio tūris. Mažėjant W/B santykiui, didėjant RHA kiekiui ir skiedinio tūriui AEA dozė turi būti didinama norimam oro kiekiui pasiekti. Taip pat faktinės AEA dozės atitiko suskaičiuotas betonų AEA dozes. Tarp faktinių ir suskaičiuotų AEA dozių gauta labai gera koreliacija. Ji rodo, kad AEA dozę, reikalingą numatytam oro kiekiui betone pasiekti, galima skaičiuoti pagal ekvivalentinį oro kiekį šio betono skiedinio dalyje.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it