Impacts of Material Orthotropy on Mechanical Behaviors of Asphalt Pavements
Bibliographic record
Abstract
Material anisotropy significantly impacts the mechanical behaviors of asphalt pavements. However, most current asphalt pavement design methods treat the material properties only as isotropic, which could significantly skew the mechanical behaviors. There is a need to evaluate the impact of material anisotropy on pavement mechanical behaviors. In this study, we first developed a new and efficient 3-dimensional finite element (FE) model of anisotropic material. Then, the feasibility of the proposed FE model was verified using field data collected with a falling weight deflectometer. Finally, using this model, the contributions of each layer anisotropy to the mechanical properties were determined. The results showed that the mechanical behaviors were more sensitive to the orthotropy than to the transverse isotropy of the material. The all-layer orthotropy was the most unfavorable combination. In addition, the subgrade orthotropy showed the most significant effect on increasing the surface deflection and compressive strain of the subgrade top (by about 10%). Based on the study results, we recommend that the homogeneity degree of the filling subgrade should be strictly controlled to ensure adequate pavement capacity and anti-rutting performance during construction.
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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 | 0.000 | 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".