Deficiency Analysis of As-Built Database to Enhance a Pavement Management System
Bibliographic record
Abstract
The ability to systematically collect and record as-built data for pavement layers benefits highway departments in many aspects. A well-established database of as-built pavements provides a tool for maintaining an up-to-date corporate record of the physical pavement structures, keeping track of unit construction costs, and reducing the amount of pavement excavations during pavement investigations. A reliable as-built database also provides inputs for falling weight deflectometer analysis and calibrating pavement performance models in pavement management systems (PMSs). A task within the development of the second-generation PMS for the New Jersey Department of Transportation is to review and analyze the existing pavement as-built database for completeness and quality. A computer program was developed to scan and categorize the as-built data into five status levels: complete, partially complete—missing original construction data, partially complete—missing recent rehabilitation data, questionable data, and no data. The results of this analysis can be used to recommend improvement for the data-collection process and to guide further investigations, such as coring and ground penetration radar tests. The general approach used in the analysis is described, data status levels are defined, results for distribution of the pavement as-built data are provided, and the significance of the analysis for PMSs is discussed. Recommendations for improving completeness and quality of the pavement construction history database also are provided.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".