Lessons Learned by Canadian Practitioners in Interpreting and Applying Pavement ME Design Results
Bibliographic record
Abstract
The adaption of AASHTOWare’s Pavement ME software program in Canada has been an evolving process. The complexity of the program at relatively large expense in annual licensing fees and staff training are factors that have affected the ready acceptance of the software. Other factors that have slowed the implementation process in Canada include: the time and resources required to learn how to use the program; understanding the input requirements; limited Canadian experience in using the program; and developing a level of confidence in the ability of the models to adequately predict pavement distresses. The need to calibrate the models for local conditions has been well documented; however, many Canadian agencies simply don’t have the budget and resources available to undertake such an exhausting venture. In Canada, the initial process to calibrate the program for Canadian conditions started through a TAC pool fund study, but as resources became limited agencies were left on their own to continue the process. With the development of their Default Parameters for AASHTOWare Pavement ME Design Interim Report, the Ministry of Transportation of Ontario (MTO) has taken the lead in implementing Pavement ME in Canada. Along with their web based GIS interface, iCorridor, this interim report has provided guidance to the pavement design community in Ontario in using appropriate default values for many of the input parameters. There are a number of tools available to help designers use this software; however, as with any software program, confidence in the program only comes in gaining experience in using it. In 2008, the TAC sponsored project to calibrate the Pavement ME software for Canadian conditions initiated a user group to provide a platform for Canadian agencies and practitioners to meet regularly and share experiences in using the software program and to help the group better understand the capabilities of the program. The primary purpose of this paper is to share the experiences of two Canadian practitioners in using the AASHTOWare Pavement ME program, discuss some of the common user complexity observed with design inputs, and demonstrate how the program can be an effective tool in the design and construction of pavements in Canada.
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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 | 0.110 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".