Sensitivity Analysis for Flexible Pavement Design Using the Mechanistic–Empirical Pavement Design Guide
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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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".