Impact of Rolled and Crushed Aggregate with Natural Pozzolan on the Behavior of HPC
Bibliographic record
Abstract
High performance concrete (HPC) has several advantages in building construction that cannot be achieved using conventional concrete in terms of strength durability, resistance to chemical attack, and workability of high performance concrete are high. The introduction of fillers and additives contributed to the improvement of high-performance concrete (HPC), other parameters also influence the performance of these HPCs, coarse aggregate fraction is known to strongly influence both fresh and hardened concrete’s properties. Consequently, selection of both content and particle size distribution for (HPC) mixture is an important issue regarding the predicted performance of concrete. For to make concrete more improvement , the porosity of the granular skeleton as well as the rheology of our HPC, 3/8 fraction of rolled gravel with smooth and rounded surfaces was introduced, in substitution for the crushed gravel of the same fraction , by testing various combinations of fractions 3/8 of rolled gravel and fraction 8/15 of crushed gravel in the present study, and based on previous results obtained the combinations 35,40 and 45% fraction 3/8 of rolled gravel and 65 , 60 and 55% fraction 8/15 of crushed gravel (binary granular system) respectively, gives a minimal porosity . Super plasticizer and four percentages of pozzolan were experimented. Compressive strength function of coarse aggregate was determined at 7, 14 and 28 days. Results have revealed that the mixtures with modified granule size distribution system ,1.5% of superplasticizer and 5% by weight of pozzolan allow an improvement in the compressive strength of 35.1% relative to the control concrete.
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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".