Effect of Dune Sand on the Properties of Reactive Powder Concrete Reinforced with Metal Fibers
Bibliographic record
Abstract
Dune sand is widely available in Algeria.Unfortunately, it is not adequately exploited, despite the interesting properties this material may exhibit.It was revealed that using this new material could significantly alleviate the pressures on the construction sector; it may also contribute to the development of the Algerian southern regions, which contain huge amounts of dune sand.It is worth mentioning that the reactive powder concrete is a novel type of concrete that possesses a high compressive strength of up to 800 MPa.This sort of concrete uses a fine particulate matter or powder that is needed for the reactions occurring between all the constituents and hence allows manufacturing high density and high-strength sand-based concrete.It should be noted that preparing reactive powder concrete requires various materials such as cement, sand, water and some additional constituents.The major part of the concrete mixture consists of fine aggregates whose characteristics play an important role in the physical and mechanical properties of the resulting material.The present study aims primarily to investigate the effects of using dune sand on the mechanical and physical properties of fiber-reinforced reactive powder concrete.In this work, river sand has been replaced by dune sand at three different substitution ratios, i.e., 40%, 50% and 60%, successively.Then, the flexural strength, ultrasonic pulse velocity, compressive strength and modulus of elasticity (dynamic and static) were examined and assessed.The experimental findings indicated that the characteristics of the resulting material were significantly enhanced when using dune sand.This allows concluding that dune sand can be employed as an effective substitute in the formulation of reactive powder concrete.Finally, the test results indicated that the ideal dune sand replacement ratio was 60%.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".