Deterioration Modelling of Visual Condition Index for Calgary’s Highway Pavement Management System
Bibliographic record
Abstract
The project is to evaluate pavement performance measures and to provide a deterioration model to improve the current asset management practice of the City of Calgary. The performance evaluation compares the pavement surface condition measurement methods used by the City and the Pavement Condition Index (PCI) approach recommended by American Society for Testing and Materials (ASTM) D6433. It includes two steps: re-calculating from raw distress data and a ground truth validation survey using images from Google Street View. The study concludes that the Highway Pavement Management Application (HPMA) Visual Condition Index (VCI) method outperforms the ASTM PCI method. Therefore, VCI decrements are the outcome of the deterioration model developed by a machine learning approach, decision tree regression. The robustness of the proposed model is validated by examining overfitting and the selected variable's consistency by the k-fold cross-validation method. The result of the project assured that the current practice of the HPMA system is justifiable, and the implementation of the deterioration model contributes to future practice by providing more accurate information on monitoring the network's life expectations and vulnerable communities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".