An advanced local calibration method for mechanistic-empirical pavement design
Bibliographic record
Abstract
Model calibration and validation is an important step of empirical modeling. The current local calibration (LC) method suggested by the AASHTOWare mechanistic-empirical pavement design guide (MEPDG) is defective and can result in a completely distorted prediction of pavement reliability in design. This paper proposes an advanced LC method that integrates a jackknife sampling procedure and an iteratively weighted least squares technique into one coherent LC process. The jackknife sampling method, which has been recommended by AASHTO for model validation, is now used for identifying outliers of the data in order to ensure a homogeneous population. Meanwhile, an iteratively weighted least squares method is proposed to simultaneously estimate the LC coefficients and the standard deviation functions. Two LC case studies for Ontario roads are presented: one for the bottom-up fatigue cracking models of asphalt concrete pavements and the other for the joint faulting models of Portland cement concrete pavements. The comparison with the traditional split-data method has shown the effectiveness and efficiency of the proposed method. With little modification, the method can be introduced as a general calibration-validation process for all empirical and mechanistic-empirical models in engineering disciplines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".