Effects of surface preparation, thickness, and material on asphalt pavement overlay transverse crack propagation
Bibliographic record
Abstract
Asphalt overlay has been widely used in pavement rehabilitation. The most frequently observed distress in an asphalt overlay is transverse cracking. In this study, the Texas long-term pavement performance (LTPP) specific pavement study 5 (SPS-5) test sections are highlighted. Three key factors affecting overlay cracking performance are investigated: (i) surface preparation, (ii) overlay thickness, and (iii) material. A deterioration model is developed to evaluate the effects of these factors. The deterioration process is well captured by incorporating both engineering principles and statistical modeling techniques, and the effects of the three key factors are thoroughly evaluated. The results suggest that (i) milling of existing pavement does not help reduce overlay transverse cracking if existing cracking is not completely removed, (ii) thicker overlay contributes to transverse crack resistance, (iii) Texas type C asphalt mixture is more effective in resisting transverse cracks than type B asphalt mixture (with coarser aggregate and less binder than type C), and (iv) overlays that incorporate reclaimed asphalt pavement (RAP) are more prone to transverse cracking than virgin asphalt. Furthermore, these effects are quantified based on the model estimation results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".