Improving the Mechanical Properties of Dune Sand for Construction Purposes by Using Nanosilica and Portland Cement as Additives
Bibliographic record
Abstract
Dune sand is available in large quantities in different areas in the world.It can be easily collected from the site with minimal excavation and labor efforts and as a sequence this makes it a good candidate in construction.To be used as construction material for engineering work and to save the environment by reducing the number of quarries and fuel consumption needed to extract the traditional construction materials such as limestone aggregates, dune sand needs to be mixed with other additives in order to get the desired strength and capacity.In this work, an investigation program was conducted on dune sand collected from the eastern part of Saudi Arabia in order to explore the sand mix engineering behavior.The sand was mixed with 5% of Portland cement and different percentage of nanosilica (NS= 0%, 2%, 4%, and 6%) and tested at different curing time (7, 14, and 28 days).The major performed tests were the unconfined compressive strength (UCS) and California bearing ratio (CBR).The results showed that the UCS, CBR, and elastic modulus (Es) of the treated sand are improved by adding the cement and nanosilica, with significant improvement when the mix tested after 28 days of curing.The effect of NS on the mechanical properties of the treated sand is more effective at high percentages (> 4%).Useful and practical relationships among UCS, CBR, and Es with high correlation coefficients, R 2 , were developed at different percentages of NS and curing time.
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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 | 0.001 | 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".