Experimental investigation and evaluation of the compactness and moisture damage of asphalt mixes incorporating dune and river sand
Bibliographic record
Abstract
Road construction is mainly based on the use of raw materials that must be in compliance with the standards, thus ensuring the quality and durability of the road. The use of dune sand and river sand in road geotechnics is an interesting subject. Both types of sand can be used in road construction and maintenance for a variety of applications. Dune sand is often appreciated for its uniform grain size and drainage capacity, while river sand can offer good mechanical strength. The majority of common bituminous mixes contain fillers made of quarry sand, whose amounts are difficult to regulate because of the variety of rock deposits and the conditions under which they are manufactured. In this paper, the compactness and moisture damage of asphalt mixes with two sand types, River sand (RS) with (0/4) size was sourced from the valley in the province of Medea (Algeria) and Dune Sand (DS) with a particle size of (0/0.5) was obtained from a dune in the Algerian province of Djelfa, were examined. Furthermore, a 100% replacement rate by weight of Crushed Sand (CS) with (0/3) mm size was used (quarry sand). The investigation employed a comprehensive approach, utilizing Marshall and gyratory shear compaction tests to assess compactness, while moisture damage was evaluated through rigorous water resistance testing and compressive strength methodology. The results of the study reveal a notable disparity in the mechanical performance of asphalt mixtures containing dune and river sand, showcasing diminished compactness and heightened susceptibility to moisture-induced damage when compared to alternative mix formulations. These findings underscore the critical role of sand type selection in asphalt mix design, emphasizing the need for careful consideration to optimize performance and durability.
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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 | 0.000 |
| 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".