EVALUATING FACTORS INFLUENCING ASPHALT ROAD CONSTRUCTION QUALITY IN HIGH TEMPERATURE CONDITION (CASE STUDY)
Bibliographic record
Abstract
Libya use a volumetric analysis in designing asphalt mixtures, which can also be upgraded in hot weather. However, the condition of some roads was further down than the required level. Rutting is one of the major defects that occur in asphalt pavements in the southern desert of Libya and severely influence the drive-ability. A questionnaire surveys and laboratory experiments were performed for a few mixes under representative temperature and traffic load. In laboratory, rutting test conducted on two different asphalt mixtures. The first mix used an asphalt binder B (60/70) at optimum bitumen content, another mixture developed using the Superpave design procedure with the same materials and performance asphalt binder grade PG (70-10). The questionnaire survey was distributed to 55 engineers and specialists in the field. The interview was conducted to a few others and the factors that leading to poor performance of asphalt roads were listed. Considering and improving of these factors will play an important role to improve the pavement performances, longer service life and lower maintenance costs. Asphalt concrete pavements (ACP) should use asphalt binder which is less affected by pavement temperature change and traffic load. The properties of the mixture, in turn, affect the pavement performance. Keywords - asphalt mixtures, consistency, performance, (PG), construction.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".