Potential Use of Friction Data from LTPP Database in Preliminary Maintenance Decisions for Asphalt and Concrete Pavements
Bibliographic record
Abstract
Friction data from the Long-Term Pavement Performance Program (LTPP) database were analyzed to examine their quality and potential use. Data from thirty USA states along with Puerto Rico, Ontario, and Saskatchewan were sorted by availability of traffic information, testing temperature, and pavement type. Some testing information was not available in the LTPP database including the tire type used and the wheel path that was tested. Traffic data in the form of Equivalent Single Axle Load (ESAL) are available in the LTPP database but are not for the entire life of the pavement and had to be supplemented with projections. There is evidence of good quality control in Friction Number (FN) measurements. FN difference between beginning and ending of section measurements was found consistent with American Society for Testing and Materials (ASTM) standards. FN distribution from the LTPP database can be used as a pavement management tool to estimate the percentage of sections that need maintenance for friction restoration. There is a great deal of variability in the FN data in spite of correcting for seasonal variations. Many of the selected sections displayed an expected trend but much of the variation in the FN trend occurred at lower values of ESAL indicating factors other than traffic being responsible for the variation. Reasonable trends took both linear and nonlinear forms but appeared nonlinear in general. The variation of FN followed a similar trend with both pavement age and ESAL. Data not available in the LTPP database may limit the potential use and modeling of the existing data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".