Use of fine aggregate matrix to analyze the rheological behavior of cold recycled materials
Bibliographic record
Abstract
Abstract Nowadays, one of the main challenges to a wider application of cold recycling techniques is the lack of reliable information on the mechanical behavior of cold recycled materials (CRM). In this context, measurement and modelling of the complex modulus of CRM mixtures may give an important contribution to the design and analysis of pavements including cold recycled layers. In this study, we analyzed the rheological behavior of CRM mixtures produced using bitumen emulsion and cement through the study of their fine aggregate matrix (FAM). Starting from a fixed CRM mixture composition, we compared different FAM mortars, focusing on the effect of water and air content. Then, we selected a composition as representative of the FAM in the mixture and investigated the evolution of both materials during a fixed curing period. Next, we measured the complex modulus of the CRM mixture and FAM at two curing stages and applied a rheological model to simulate and compare their behavior. Results showed that the properties of CRM mixtures are comparable to those of FAM mortars produced using all the binding agents (bitumen emulsion and cement) and a fraction of the voids contained in the mixture. Despite the huge difference in volumetric compositions, the FAM mortar controlled the curing and the thermo-rheological behavior of the CRM mixture, while the coarse reclaimed asphalt aggregate fraction and the voids mainly affected the asymptotic properties (equilibrium and glassy moduli) and the non-viscous dissipation component.
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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 | 0.001 | 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".