Using Cement as Filler to Enhance Asphalt Mixes Performance in Hot Climate Regions
Bibliographic record
Abstract
This paper investigates the addition of different percentages of ordinary Portland cement as a filler in conventional asphalt concrete for a range of heavy traffic. Road pavement agencies in hot areas face the daunting challenge of preserving their pavements in a fair to good condition to increase their lifespan. This challenge is due to the high occurrence of permanent pavement deformation via rutting, which is one of the major distress factors influencing pavements. This is a particularly serious issue in hot and arid countries which are closely associated with various aggravating factors. These aggravating factors include the choice of bitumen binder viscosity, the type of bitumen, the available low-quality materials, and the high environmental temperatures. Ultimately, poor performance will show within the first few years of service as permanent deformations such as rutting, shoving, and depressions. The examined properties include the resilient modulus and the resistance to rutting. Findings indicate that the resistance to rutting and the rigidity of the asphalt concrete are both substantially increased as the cement content is increased. Moreover, to meet the heavy traffic spectrum requirements, increasing the embedded cement content in the asphalt concrete improves pavement structural capacity. Finally, based on the rigidity expected for different cement levels, design curves are provided for pavement design in hot climates using low quality aggregate materials.
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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".