Nonlinear viscoelastic approach to model damage-associated performance behavior of asphaltic mixture and pavement structure
Bibliographic record
Abstract
This paper presents an integrated experimental–numerical effort to more accurately model the damage characteristics of asphalt mixtures and pavement structures than conventional elastic and (or) linear viscoelastic approaches can. To this end, Schapery's nonlinear viscoelastic constitutive model was implemented into a finite element software via user defined subroutine (user material, or UMAT) to analyze an asphalt pavement subjected to heavy truck loads. Then, a series of creep and recovery tests were conducted at various stress levels and at different temperatures to obtain the stress-dependent and temperature-sensitive viscoelastic material properties of asphalt mixtures. With the viscoelastic material properties characterized and the UMAT code, a typical pavement structure subjected to repeated heavy truck loads was modeled with the consideration of the effect of material nonlinearity with a realistic tire loading configuration. Three-dimensional finite element simulations of the pavement structure present significant differences between the linear viscoelastic approach and the nonlinear viscoelastic modeling in the prediction of pavement performance with respect to rutting and fatigue cracking. The differences between the two approaches underline the importance of proper and more realistic characterization of pavement materials and should be addressed in the process of performance-based pavement design.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".