Development of a Correlation between the Resilient Modulus and CBR Value for Granular Blends Containing Natural Aggregates and RAP/RCA Materials
Bibliographic record
Abstract
Limited supplies of natural aggregates for highway construction, in addition to increasing processing costs, time, and environmental concerns, have led to the use of various reclaimed/recycled materials. Reclaimed asphalt pavement (RAP) and recycled concrete aggregate (RCA) have prospective uses in substantial amounts in base and subbase layers of flexible pavement in order to overcome the increasing issue of a shortage of natural aggregates. This research presents the development of an empirical model for the estimation of resilient modulus value (MR) on the basis of CBR values using experimental results obtained for 52 remoulded granular samples containing natural aggregates, RCA, and RAP samples. Statistical analysis of the suggested model shows promising results in terms of its strength and significance whent-test was applied. Additionally, experimental results also show thatMRvalue increases in conjunction with an increase in RAP contents, while the trend for the CBR value is the opposite. Statistical analysis of simulation results using PerRoad and KenPave demonstrates that addition of RAP contents in the subbase layer of flexible pavements significantly improves its performance when considering resistance against rutting and fatigue. However, results of repeated load triaxial tests show that residual accumulative strain under a certain range of loading conditions increases substantially due to the addition of RAP materials, which may be disadvantageous to the serviceable life of the whole pavement structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".