Comparison of Fatigue Cracking Performance of Asphalt Pavements Predicted by Pavement ME and LVECD Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it