Validating Spring Weight Restriction Limits with Mechanistic–Empirical Failure Prediction Models
Bibliographic record
Abstract
This study developed local mechanistic–empirical failure models to predict fatigue and rutting damage on spring-weight-restricted (SWR) roads in Manitoba, Canada. The local models were used to assess the SWR load limits and to validate the current SWR deflection levels that regulate commercial vehicle weights during the spring period. The local model predictions were compared with the damage models of the Asphalt Institute (AI) and the Mechanistic–Empirical Design Guide (MEPDG). The results indicated that the predicted equivalent single-axle load repetitions to fatigue failure from the local model were higher than those of the MEPDG and the AI models by an average of 37% and 20%, respectively. On the basis of the local model equivalent single-axle load predictions, the current SWR deflection limits used in Manitoba appear to be a reasonable method for regulating B-Train operations during the spring period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".