Formulation of Traffic Inputs Required for the Implementation of the M-E PDG in Data-Scarce Regions: Lebanon Case Study
Bibliographic record
Abstract
The implementation of the Mechanistic-Empirical Pavement Design Guide (M-E PDG) in regions outside the United States and Canada, such as the Middle East and North Africa region (MENA), is still in its early stages due to the scarcity of the design input data required for its use at high reliability levels. Several studies have been carried out to present correlations for obtaining material properties as well as exploring the use of M-E PDG embedded climate files to account for the missing inputs and different environmental conditions. Yet, the main challenge resides in obtaining adequate traffic data and adapting local traffic inputs for M-E PDG default values. This paper presents guidelines for developing truck classification and growth factors from short-term traffic count surveys for countries where historical traffic data are unavailable or insufficient. The case of Lebanon is tackled as a case study for demonstration. The sensitivity of the pavement response to the variation of the extrapolated traffic input data is studied under different climatic and material conditions and validated against recent characterized and categorized traffic data. The results reveal that highway design agencies can process traffic count surveys to convert them into traffic input data as elaborated in the methodology, as the pavement’s performance is not majorly affected by the variations and assumptions used in the calculations of the truck classes. However, the variation in the truck traffic volume, i.e., growth factor, significantly influences the predicted pavement distresses, which necessitates the continuous collection of traffic data to have more representative values for the growth rate estimation. Based on the obtained results, final recommendations are presented for the implementation of the M-E PDG in regions lacking traffic records.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".