Pavement Deterioration Model Using Markov Chain and International Roughness Index
Bibliographic record
Abstract
Abstract Pavement deterioration leads to drop in serviceability and possibly failure of pavement sections due to initiation and expansion of distresses such as cracks and rutting. This paper aimed at predicting the future condition of pavement sections based on Markov chain model and the international roughness index (IRI). Developing this model can facilitate life cycle analysis and selecting the correct treatment at the right time. The historical IRI data of Canadian pavement sections were collected from Long term pavement performance (LTPP) database. IRI values were used to assess condition of the pavement sections based on the recommended ranges by the Federal highway administration (FHWA). The transition probabilities were estimated using the percentage prediction method based on historical condition data extracted from the LTPP. These probabilities are assembled in a transition probability matrix essential for the Markov chain model. The developed matrix can be used to forecast pavement conditions after any number of transition periods. The developed method assists in predicting pavement performance and facilitates the decision-making process. The method is applied to a real case study to examine its validity. The model can be expanded further by considering additional data from additional pavement networks.
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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 | 0.002 |
| 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) | 0.004 | 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".