Life cycle costing analysis using the mechanistic-empirical pavement design guide for flexible pavements
Bibliographic record
Abstract
The Mechanistic-Empirical Pavement Design Guide (MEPDG), developed by the American Association of State Highway and Transportation Officials (AASHTO) under the directive of the U.S. National Cooperative Highway Research Program (NCHRP) Project 1-37A, is the latest development in the concept and theories for the analysis and design of new pavements and of overlays for the existing pavements. While MEPDG is waiting for its full-scale implementation and to replace the traditional pavement design methods, it is desirable to make use of the performance prediction capacity of the MEPDG for accurate life-cycle costing analysis. The objective of this study is to review the state of the art and state of the practices for LCC and the new MEPDG methodology for flexible pavement design/preservation, and explore a framework for the integration of LCC into the new MEPDG, which would help the pavement agencies to evaluate the most economic (cost-effective) flexible pavement design for a new roadway section and overlay design for an existing flexible pavement as well as the preservation (maintenance and rehabilitation) time/strategy based on MEPDG methodology.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".