Thermo-mechanical Properties of Stone Mastic Asphalt (SMA) - Experimentation and Rheological Testing
Bibliographic record
Abstract
Although a high content of unmodified binder is used, Stone Mastic Asphalt (SMA) generally shows good resistance to rutting due to the stone-to-stone contact. In Quebec, a test section was built in 2004 to evaluate the performance of SMA used as overlay for highly cracked pavements. Experimental results on the thermo-mechanical behaviour of SMA are presented herein (Complex modulus, Thermal coefficient, direct tension and compression tests and Thermal Stress Restrained Specimen Test-TSRST). The DBN model (Di Benedetto and Neifar) based on a thermo-visco-elasto-plastic approach was adopted for the modeling of experimental data. Numerical simulations show the need to consider non-linear behaviour for an accurate prediction of stress evolution in the TSRST. The DBN model is also used to evaluate the variation of the thermal stress under sinusoidal temperature variation with a 24-hour period. For the first 3 to 5 cycles, the maximum thermal tensile stress at higher cycles increases comparing to the previous cycles. Beyond that, the maximum tensile stress stabilizes. This phenomenon is important for Quebec environment during thaw periods where temperature variations are relatively high.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".