Impact Analysis of Individual Distresses on Overall Pavement Condition Assessment
Bibliographic record
Abstract
The Ontario Ministry of Transportation (MTO) uses the Distress Manifestation Index (DMI), a ten-point scale based on visual assessment of disaggregated pavement distresses, to supplement laser based high-speed roughness measurement when determining overall pavement condition. The current practice is to visually observe and rate the severity and density of each individual distress along a pavement section; this produces a subjective rating with some variation between individual evaluators. The process is time-consuming, as the entire provincial highway network must be travelled and rated visually. The paper examines several alternatives to the current DMI scale, including scales that omit lightly weighted types of distresses and scales that only use cracking and rutting distress types. The purpose of this study is to determine whether reducing the DMI calculation to include only a subset of distresses will have a significant impact on the quality of the performance measures. Particular interest in cracking and rutting distresses is fuelled by the development of automated laser-based systems that can evaluate the cracking and rutting on the pavement at highway speeds. Automating the process would reduce the time and cost of the pavement distress survey. The long-term goal is to reduce sources of variation by simplifying the DMI, and/or delegating the rating process to automation. Preliminary results show that the modified DMI scales are acceptably accurate, and the automated collection of DMI data shows potential.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".