Evaluation of Semi Automated-Automated Pavement Condition Surveys - An Ontario Field Study
Bibliographic record
Abstract
Pavement management systems (PMS) rely on consistent and repeatable distress data. Traditionally, such data has been collected through manual surveys, which are subjective, tedious and time consuming. Although this method of collection is very important, particularly at the project level of PMS, there are many safety and other advantages to using semi automated or automated distress measurements for network level PMS. Ideally, the data would be collected at travel or high speed, using state-ofthe- art image capture equipment. This report is directed at evaluating the applicability of using semi automated and automated distress collection techniques for the Ministry of Transportation Ontario (MTO). To accomplish this, a review of the available high speed data collection technologies was carried out in addition to a full field study experiment which involved the evaluation of 37 asphalt concrete, Portland cement concrete, surface treated and composite pavements were investigated. This study involved an evaluation of the best technologies of three highly skilled service providers, namely Applied Research Associates, Roadware, and Stantec, and compared their evaluations with the traditional MTO manual surveys. All three service providers donate their time and equipment to this project and both the research team and MTO gratefully acknowledge this assistance. Accordingly, a research plan and team have been put together at the University of Waterloo to tackle the basic challenges of whether semi automated/automated distress surveys can replace the traditional visual rating method. The work plan involved a series of tasks starting with a comprehensive literature review, progressing to an identification of the most promising technologies and then the design and execution of a field experiment to compare and assess the automation technologies with the manual method. This report summarizes various statistical analyses which include individual distress comparisons, company versus company evaluations, manual versus automated and various distress manifestation index comparisons. In short, although the findings are encouraging, it would be difficult to adopt these technologies in the current Request for Proposal (RFP) environment before MTO rationalizes distresses. It would be suggested that following a reduction in the number of required distresses and a reduction in the number of severity and density levels that the technology could be adopted through the use of proper quantity assurance and benchmarking technologies
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".