Integration of Ramps into Pavement Management Systems
Bibliographic record
Abstract
Ramps constitute an essential part of an agency highway network. They not only provide access to the mainline highway network, but some ramps also may be of sufficient length to be treated as a highway segment. Ramps can deteriorate faster than mainline routes, with resulting safety issues and discomfort to motorists. To be maintained in the same fashion that highway agencies maintain mainline pavements, ramps need to be included in pavement management systems (PMSs). A recent informal survey from 11 highway agencies in the United States and Canada showed that no agency had a formal maintenance and rehabilitation (M&R) program for ramps. All of them, on most occasions, include ramp M&R with adjacent mainline pavement M&R projects, and on a few occasions they fix ramps separately. A novel approach was developed to integrate ramps with the existing mainline network database that resulted from 8 years of PMS development and enhancement efforts. Included are the scope and methodology for the ramp survey and analysis, development of a ramp identification system, a condition-rating procedure for field testing of the ramp network, a ramp data-loading and data-processing procedure, and a ramp M&R and optimization analysis. Also described are the benefits of such analysis and how it can be used in other agencies to improve ramp pavements in the long term. The integration of ramps into the PMS provides the ramp's condition, needs, and budgeting summaries. The PMS modeling capabilities can be used to determine the asset value of the ramp network. Therefore, integrating ramp inventory and condition-rating data with the PMS mainline network can lead to effective ramp M&R decision making.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".