Experimental Design Based Evaluation of Sensitivities of Mechanistic-Empirical Pavement Design Guide (MEPDG) Predictions for Ontario's Local Calibration
Bibliographic record
Abstract
A Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under NCHRP Project 1-37A to address the shortcomings of empirical pavement design methods. The MEPDG uses mechanistic-empirical models to analyze the impacts of traffic, climate, materials and pavement structure and to predict long term performances of pavement. The MEPDG software (AASHTOWare Pavement M-E) use a three-level hierarchical input scheme to predict pavement performance in terms of terminal International Roughness Index (IRI), Permanent Deformation , Total Cracking (Reflective and Alligator), Asphalt Concrete (AC) Thermal Fracture, AC Bottom-Up Fatigue Cracking, and AC Top-Down Fatigue Cracking. Different highway agencies are taking initiatives to adopt MEPDG based pavement design and performance prediction by calibrating the prediction models for their local conditions. However, these inputs with different levels of accuracy may have significant impact on performance prediction and thereby on accuracy of local calibration. This study focuses on the sensitivity of the input parameters of MEPDG distresses to identify the effect of the accuracy level of input parameters based on orthogonal experimental design. A local sensitivity analysis is carried out by using Ontario’s default value and historical performance record of Ontario highway system. Sensitive input parameters are evaluated through a multiple regression analysis for respective distresses. It is found that terminal IRI is sensitive to initial IRI, initial permanent deformation, and milled thickness in asphalt layer; permanent deformation is sensitive to initial permanent deformation, subgrade resilient modulus, and traffic load; top down fatigue cracking is sensitive to AC effective binder content, and AC air voids. Based on the independent influence of these sensitive inputs, the requirement of accuracy level will be identified for MEPDG design.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".