Evaluating the Impact of Specimen Shape and Size on the Strength Characteristics of Recycled Aggregate Concrete
Bibliographic record
Abstract
The use of green concrete is increasing worldwide.The growing global demand for utilizing recycled materials in concrete mixes to enhance sustainability makes it important to study the strength properties and behaviour of recycled aggregate concrete (RAC).While the impact of specimen shape and size on the compressive and flexural strength of normal concrete is well-documented, their effects on concrete mixes containing recycled aggregate and ceramic waste are not yet defined.This paper studies the effect of specimen shape and size on the strength properties of RAC by conducting compression tests on cubes and cylinders and performing four-point flexural tests on beams.Locally available recycled coarse aggregate, ceramic fine aggregate, and ceramic waste powder were used to develop the recycled aggregate concrete.Compression tests were conducted on standard cubes, standard cylinders, and halfscale cylinders.Flexural tests were performed on beams sized 150 mm×150 mm×460 mm and 75 mm×75 mm×230 mm.Based on the experimental results, it was found that the specimen shape and size significantly affect the strength properties of RAC, and the conversion factors differ from those of normal concrete with the same strength.Additionally, a preliminary relationship between the compressive strength and flexural strength of RAC is suggested.
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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 | 0.001 | 0.002 |
| 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".