Development of the Roadmap for the Implementation of the Mechanistic Empirical Pavement Design Guide in Canada
Bibliographic record
Abstract
The AASHTO Guide for Design of Pavement Structures is the principal design method used in North America. With advancements in technology, pavement design professionals across North America recently embarked on a major upgrade of pavement design methods using mechanistic-empirical (ME) procedures. The Guide for the Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures (ME PDG) allows pavement designers to improve design reliability, predict specific failure modes, better characterize seasonal/drainage effects, and reduce overall life cycle costs. The ME PDG contains procedures for the design and analysis of all types of new and rehabilitated pavement systems for evaluating existing pavements, rehabilitation treatments, subdrainage and foundation improvements. However, to benefit from the procedures provided in the Guide, the development of input parameter databases as well as local calibration of the performance prediction models will be required. The implementation of the design guide requires consideration of local environmental conditions, traffic, pavement materials and pavement performance. The authors of this paper were retained by the Transportation Association of Canada through a pooled fund study to develop an implementation roadmap for Canadian use of the ME PDG. This paper describes the process that considers the needs and the resources available to Canadian agencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".