Validation of the Mechanistic-Empirical Pavement Design Guide Using PMS Data
Bibliographic record
Abstract
The Mechanistic-Empirical Pavement Design Guide (M-E PDG) developed under the National Cooperative Highway Research Program (NCHRP) Project 1-37A integrates structural loading due to traffic with the change in material properties due to environmental effects to determine the mechanistic properties of pavement materials. These mechanistic properties are then translated to expected pavement performance through a series of transfer functions (calibration models) to estimate key pavement condition indicators over the life of the pavement. One of the key recommendations of the NCHRP study was that local calibration data should be used to validate and ‘fine tune ’ the national models that were calibrated based on long term pavement performance data from the United States and Canada. Calibration and verification of the national calibration models to reflect local conditions can be intimidating because of the large amount of material test and performance data that is required for a full calibration. However, many agencies already have a significant amount of relevant data collected as a part of their pavement management systems (PMS) which can be used to assist in validating the national 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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".