Characterizing Concrete Performance Mixed with Coarse Aggregate Sourced from Northern Saudi Arabia: A Case Study
Bibliographic record
Abstract
The coarse aggregate particles, which are a main ingredient in concrete, are typically obtained from crushed rocks or natural deposits such as riverbeds or valleys.This research aimed to explore the effectiveness of using local crushed aggregate as well as local valley aggregate as coarse aggregate in producing concrete.Several standard tests were conducted to explore the physical properties of both aggregate types, and a large number of concrete samples were prepared to test the properties of concrete made with crushed or valley aggregates.The finding revealed that the crushed aggregate exhibits higher bulk density, specific gravity, and lower water absorption compared to the valley aggregate.Additionally, the workability of fresh concrete made with valley aggregate was better than that of concrete mixed with crushed aggregate, due to the roundness and smoothness of the valley aggregate.However, concrete containing crushed aggregate exhibited better compressive and split tensile strength due to the naturally rough surface and shape angularity.Finally, the study concluded that both sources of coarse aggregate are suitable for use in concrete production, with concrete incorporating crushed aggregate slightly stronger and concrete made with valley aggregate having better workability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".