Calibration and Validation of Condition Indicator for Managing Urban Pavement Networks
Bibliographic record
Abstract
Deterioration indexes that may combine types of surface distresses, serviceability, and structural indicators are commonly used for pavement management at the network level. These indexes differ in the types of deterioration and criteria considered to quantify severity and density of distresses. Most of these indexes were developed for interurban road networks; therefore, their application to urban networks is complex and not representative. For this reason, there is a need for a better understanding of urban pavement behavior to enable collection of the distresses relevant to these types of pavements and development of an overall condition index for urban pavements that represents the mix of the more relevant distresses for use in network analysis. This study is part of a 3-year project developed in Chile: Research and Development of Solutions for Urban Pavement Management in Chile. The main objective of this study was to calibrate and to validate an urban pavement condition index (UPCI) representative of the overall condition of these pavements, according to objective measures of surface distresses and evaluations of an expert panel. The scope of this study included the development of distress evaluation guidelines for asphalt and concrete pavements considering manual and automated surveys, the application of these guidelines in different types of urban networks, and the assessment of these networks by an expert panel. Finally, three UPCI equations were obtained with satisfactory validation for asphalt pavements with manual and automated data collection and for concrete pavements with manual data collection.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".