Development of a Correlation between the Resilient Modulus and CBR Value for Granular Blends Containing Natural Aggregates and RAP/RCA Materials
Why this work is in the frame
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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 ( M R ) 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 when t- test was applied. Additionally, experimental results also show that M R value 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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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 | 0.001 | 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 | 0.001 |
| 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 it