Laboratory Investigation on the Resilient Modulus, Indirect Tensile Strength and Unconfined Compressive Strength of Cold In-Place Recycled Asphalt Mixes Using Foamed Bitumen
Bibliographic record
Abstract
There has been a dramatic growth in asphalt recycling and reclaiming as a technically and environmentally preferred way of rehabilitating deteriorated pavements over the last couple of decades. One of the latest asphalt recycling techniques is cold in-place recycling using foamed bitumen. In recent years, there have been a number of successfully implemented cold in-place asphalt recycling projects in Iran; and it seems that the utilization of this method is gaining more acceptances amongst the road authorities and the contractors. At the same time, endeavors to evaluate the performance properties of foamed bitumen mixes were started and are still in progress. The present research, studies the mechanical and the performance properties of foamed bitumen mixes. In the first phase of the project, the effect of aggregate grading, bitumen and cement content and compaction efforts, on resilient modulus and indirect tensile strength (ITS) of 144 foamed bitumen specimens (of various curing conditions) has been investigated and analyzed. In the next phase, the effect of above parameters on resilient modulus and unconfined compressive strength (UCS) of 108 different samples was studied. Results indicate that for the tested materials, changing the aggregate grading from coarse to fine didn’t have a significant effect on resilient modulus, ITS, UCS and the moisture susceptibility of the specimens. Also the effect of increasing bitumen content from two to four percent is negligible. On the contrary, adding one percent cement and increasing it to two percent considerably improved the moisture susceptibility and the mechanical properties of the mixes. Based on the obtained data, a regression model between resilient modulus and ITS of the foamed bitumen mixes was developed to facilitate the prediction of the resilient modulus based on the corresponding ITS values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".