Calibrating Mechanistic–Empirical Pavement Design Guide for North Carolina: Genetic Algorithm and Generalized Reduced Gradient Optimization Methods
Bibliographic record
Abstract
The Mechanistic–Empirical Pavement Design Guide (MEPDG) is the state-of-the-practice pavement analysis software developed under NCHRP Project 1–37A. Recently, AASHTO announced the first commercial version of the software, DARWin-ME, to replace the 1993 AASHTO design guide DARWin software. The MEPDG and DARWin-ME use similar models for predicting rutting and bottom-up fatigue cracking. Both distress models were nationally calibrated with measured performance data collected from hundreds of long-term pavement performance sections across the United States and Canada. Verification work indicated that these nationally calibrated models did not reflect North Carolina's local materials, construction practices, and local traffic. Therefore, the performance models must be recalibrated to reflect local conditions. The scope includes rutting and alligator cracking in flexible pavements. The development of rutting and fatigue model coefficients (k-values) is investigated for 12 commonly used hot-mix asphalt (HMA) mixtures in North Carolina, and two approaches for recalibrating the rutting and fatigue cracking model coefficients (β-factors) are compared to reflect local materials and conditions. The two optimization methods evaluated are generalized reduced gradient (GRG) and genetic algorithm (GA) optimization. Results indicate that rutting and fatigue cracking k-values for North Carolina HMA mixtures are generally different from national averages. The GA optimization method does a better job of predicting local distresses than do the GRG method and nationally calibrated models.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".