Bibliographic record
Abstract
Pavements are subjected to repeated traffic and environmental actions. These actions lead to the degradation of pavement and affect the pavement performance. The assessment of pavement performance is important not only for selecting pavement design parameters but also for choosing pavement maintenance and rehabilitation strategies. The traffic loads, the environmental actions, and the properties of pavement material are uncertain. These uncertainties must be taken into account when predicting the pavement performance. Since the traffic and environmental actions vary with time, they should be modeled as stochastic processes. In this study, stochastic models of the net traffic load growth and environmental actions are proposed based on the rectangular pulse processes. These models can be used in conjunction with the Ontario Pavement Analysis of Cost (OPAC) model to predict flexible pavement performance in a probabilistic framework. The prediction of pavement performance for a typical pavement was carried out by using the simple simulation technique and the first-order reliability method. The analysis results suggest that the predicted pavement serviceability measured in terms of the riding comfort index depends on the correlation between traffic effects in each year and the correlation between the environmental actions in each year. The results also suggest that the uncertainty in the riding comfort index is controlled by the uncertainty in traffic and environmental actions in the early stage of service, while it is dominated by the material property of the subgrade in the later stage of service.Key words: deterioration, reliability, pavement, serviceability, stochastic process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".