Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management
Bibliographic record
Abstract
Existing performance models developed for interurban pavements are not applicable to urban pavements due to differences in traffic demands and deterioration trends. The objective of the study was to develop performance models for the management of urban pavement networks. Markov chains and Monte Carlo simulation were applied to account for the probabilistic nature of pavements deterioration over time, using data collected in the field. One of the advantages of this methodology is that it can be used by local agencies with scarce technical resources and historical data. Eight performance models were developed and successfully validated for asphalt and concrete pavements in humid, dry and Mediterranean climates with different functional hierarchies. The resulting models evidence the impact of design, traffic demand, climate and construction standards on urban pavements performance. Predicted service life of asphalt and concrete pavements in primary networks are consistent with design standards. However, pavements in secondary and local networks present shorter and longer service life compared to design life, respectively. Climate is a relevant factor for asphalt pavements, where higher deterioration was observed compared to that expected. Opposite to this, no relevant differences between design and performance can be attributed to climate in concrete pavements.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".