CALIBRATING MECHANISTIC-EMPIRICAL PAVEMENT PERFORMANCE MODELS WITH AN EXPERT MATRIX
Bibliographic record
Abstract
Pavement performance modelling is an important element in the proper management of pavement infrastructure. Various factors such as material properties, traffic loads and climate plus construction and maintenance schedules must be utilized to develop performance prediction and life-cycle costs. This paper describes a methodology for the calibration and validation of a mechanistic-empirical flexible pavement performance model. Mechanistic-empirical design methods combine theory based design such as calculated stresses, strains or deflections with empirical methods in which a measured response is related to structural thickness and pavement performance. The design system presented incorporates elastic layer analysis to determine pavement response. It uses cumulative ESALs, subgrade type and layer thickness to determine the most effective design. The input and output variables are probabilistic and they have been re-calibrated to extend beyond the range of applicability to the more extreme conditions (i.e. extremely low traffic volumes or extensive heavy truck traffic loading). The new mechanistic-empirical model also separates the environment and traffic effects of performance. In effect, the total pavement performance (P) is the cumulative effect of the damage due to the environment and the damage due to traffic. Hence, the regional differences (eg. between Southern and Northern Ontario in the example) can be quantified. The system calculates either roughness in terms of the International Roughness Index (IRI) or Riding Comfort Index (RCI) or in terms of performance as a Pavement Condition Index (PCI). Although this system was developed for Ontario conditions, the mechanistic-empirical performance model can be re-calibrated to apply to other conditions. Examples are provided to show the relative deterioration/performance curves for various design situations. For the covering abstract of this conference see ITRD number E201066. (A)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".