Evaluation of Semiautomated and Automated Pavement Distress Collection for Network-Level Pavement Management
Bibliographic record
Abstract
The Ministry of Transportation Ontario (MTO) and the University of Waterloo examined the feasibility of using automated pavement distress collection techniques in addition to data collected through manual surveys. Test sections including surface-treated, asphalt concrete, composite, and portland cement concrete pavement structures in 37 locations in southern Ontario, Canada, were evaluated. Distress manifestation index (DMI) values were computed for each section by MTO pavement design and evaluation officers using the manual evaluation data collected. DMI values were then computed for each section by using automated distress evaluation data. Before DMI values could be computed, the relevant data had to be extracted and verified, and the distress data had to be categorized. DMI values computed from data collected manually and by using automated systems were compared. Finally, a repeatability analysis was performed on both the manual and the automated techniques. Results indicate no significant differences among sensor-based equipment; however, there are significant differences among measurements obtained from digital image-based technology. The implications of such outcomes are discussed, including the specifics regarding methodology implementation in order to encourage practitioners to benefit from the preliminary investigation. Current available techniques can provide MTO with valuable information for pavement management purposes. The automated results are comparable with manual surveys. However, these surveys should be supplemented with manual surveys, especially for design purposes, because some of the pavement distresses were difficult to identify with the automated methods.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".