Characterization of Cold In-Place Recycled Materials at Young Age Using Shear Wave Velocity
Bibliographic record
Abstract
Abstract The characterization of cold recycled pavement materials at an early stage of their life, right after compaction, is difficult, especially if classical tests are used. Indeed, these materials at a very young age behave like granular materials, which affect the feasibility of the usual tests done on bituminous materials. Nondestructive techniques using wave propagation can be used to overcome this difficulty. The aim of this study is to evaluate if a method based on the spectral analysis of mechanical shear wave generated by piezoelectric rings (P-RAT method) can be used to characterize a cold in-place recycled material treated with an asphalt emulsion at a young age. Shear waves are used here because of the water content of such materials at early age. Such material can contain 10 % of water by volume before compaction. Shear wave allows the characterization of the skeleton of aggregates and bituminous binder (i.e., the asphalt concrete) with no interference from the pore water, thanks to the zero shearing resistance of the water. The tests show a strong link between water disappearance inside the specimen during the cure and the evolution of shear wave propagation velocity in the specimen. Moreover, water disappearance can be easily related to the evolution of |E*| in the specimen, allowing the characterization of this material using the evolution of the shear wave propagation velocity.
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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".