Sensitivity Analysis for Flexible Pavement Design Using the Mechanistic–Empirical Pavement Design Guide
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
Over the last few years, transportation agencies have had the opportunity to use AASHTO’s interim Mechanistic–Empirical Pavement Design Guide (MEPDG) software. This software allows users to assess the impacts of traffic, climate, materials properties, etc. on the predicted pavement performance. Several transportation agencies have begun the process of implementing the design process. However, many agencies are just starting the implementation process or are waiting to see the results from other states. As such, the Transportation Research Board Flexible Pavement Design Committee (AFD60) requested assistance from state agencies in collecting and disseminating information and results related to sensitivity analysis of flexible pavement designs performed by transportation agencies. A survey similar to the Federal Highway Administration (FHWA) MEPDG survey used earlier in the decade was circulated via electronic mail during the summer of 2009. The survey questions and summary of responses are provided in this presentation. Overall, there were 52 agencies that participated in the study, including 48 out of the 50 U.S. states. The other agencies were the District of Columbia Department of Transportation, Puerto Rico, FHWA Federal Lands Division, and Ontario Ministry of Transportation. A remarkable response rate of 98% was attained.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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