Linear viscoelastic (LVE) properties of asphalt mixtures with different glass aggregates and hydrated lime content
Bibliographic record
Abstract
In this paper, the results of a research project examining the effect of glass aggregate and hydrated lime content on linear viscoelastic (LVE) properties are presented. Three glass aggregate contents (0%, 20% and 60%) and two hydrated lime contents (0%, 2%) were studied for a total of six different asphalt mixtures. All mixtures were fabricated in the laboratory using a PG70-28 polymer-modified binder. LVE properties were measured with the complex modulus (E*) test (tension compression on cylindrical specimens) at different temperatures (−35°C to +35°C) and frequencies (0.01 Hz to 10 Hz). Experimental E* test results were modelled with the 2S2P1D model. The Partial Time-Temperature Superposition Principle (PTTSP) was applied with good precision. Differences in terms of LVE properties were found for mixtures with glass aggregate compared with conventional mixtures. The glassy modulus, as well as the complex modulus norm, was decreased due to the glass aggregates. Moreover, the normalisation of the E* results showed that adding 60% glass changes the LVE properties. No notable effect related to the hydrated lime content was observed.
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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 | 0.000 |
| 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) | 0.001 | 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".