Quality Management of Network-Level Pavement Condition Data Collection: Current Methods and Practices
Bibliographic record
Abstract
Over the past three decades, most highway agencies have adopted Pavement Management Systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS relates to collecting network-level pavement condition information and, as a result, agencies are faced with developing procedures and guidelines for quality management of these activities. The quality of collected data has a direct impact on the utility of, and confidence in the PMS, which in turn has an effect on the use of scarce resources for preserving highway networks. However, until recently there was little attention given to the quality and consistency of collected data. The Long Term Pavement Performance (LTPP) program, in conjunction with advances in automated methods of collecting many types of pavement condition data, focused new attention on the topic of pavement condition data quality management. This attention on quality management has led some agencies to develop methods and practices to ensure the appropriateness of the collected data for use in their respective PMS, but these practices are far from universal and many agencies lack a formalized data collection quality management plan. The objective of this paper is to summarize current quality management practices being employed by public road and highway agencies for both automated and manual pavement condition data collection using information collected in a recent survey of 55 highway agencies throughout the United States and Canada.
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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 | 0.139 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".