Comparison of Fatigue Cracking Performance of Asphalt Pavements Predicted by Pavement ME and LVECD Programs
Bibliographic record
Abstract
Mechanistic–empirical pavement design has received significant attention from the pavement community as the method for designing asphalt pavements in the future. Currently available software for mechanistic–empirical pavement design includes the AASHTOWare Pavement ME Design (Pavement ME) program. The Pavement ME program allows users to predict pavement distresses by applying layered elastic theory for the mechanical responses and using empirical models for the distress predictions. The layered viscoelastic pavement design for critical distresses (LVECD) program, which employs three-dimensional viscoelastic finite element analysis with moving loads, can also be used to predict the fatigue and rutting performance of pavements. The LVECD program employs the simplified viscoelastic continuum damage (S-VECD) model as the material model for the fatigue performance predictions of asphalt mixtures under complex loading and environmental conditions. This paper examines and compares the performance of 33 pavement sections from five research projects located in the United States, Canada, and South Korea by using both the Pavement ME and LVECD computer programs. To verify the results obtained from these two programs, the simulations were compared with the field performance data. In terms of ranking, the LVECD simulations provided better agreement with the field performance data than did the Pavement ME simulations. One of the main reasons for the better predictions obtained by the LVECD program is that its fatigue performance predictions depend on the mixture properties of all the layers, whereas the Pavement ME program considers the fatigue properties of only the bottom layer mixture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".