Comparative Analysis of Pavement Rehabilitation Designs Using AASHTOWare Pavement ME, AASHTO 1993 and Surface Deflection Methods
Bibliographic record
Abstract
Currently, the majority of the highway projects in Manitoba involve the rehabilitation of the existing pavements. Asphalt concrete (AC) overlays with levelling, milling, cold in-place recycling (CIR) with expanded asphalt and pulverization of the existing AC or rubblization of existing portland cement concrete (PCC) are the common rehabilitation practices. Manitoba uses the surface deflection based and/or the AASHTO 1993 methods for these rehabilitation designs and is currently evaluating the new AASHTOWare Pavement ME design method. This paper presents a comparative analysis of the required AC overlay using these three design procedures for the above mentioned options. The suitability of the globally calibrated rutting and roughness models and the potential for successful calibration are also discussed. Results show that the deflection based and Pavement ME Design methods provided comparable overlay structures for the selected projects. However, establishing a reasonable target value for each distress in the Pavement ME is necessary for comparable overlay thicknesses. The Pavement ME Design program under predicted the rutting for two projects with straight overlays. However, it over predicted the rutting for these projects for the milling and overlay option. These results are unexpected and raise the question as to whether a local calibration effort will be effective. The CIR should be considered as an AC layer for a reasonable overlay thickness using the Pavement ME Design program. Using the Pavement ME Design program, the required AC thickness for the new construction is higher than that required for the rehabilitation which is also questionable.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".