Incorporating Maintenance and Rehabilitation History into Pavement Performance Modeling for Jointed Plain Concrete Pavement
Bibliographic record
Abstract
In order to ensure good quality and well-maintained road, regular maintenance and rehabilitation (M&R) of pavement is mandatory. The Long Term Pavement Performance (LTPP) database has the most comprehensive pavement performance data along with its M&R history for more than 2,500 pavement sections throughout the United States and Canada. The artificial neural networks (ANNs) modeling approach has been used in recent years for the prediction of pavement performance. However, most pavement performance modeling does not consider the M&R history in the model development. As such, this paper aims to exhibit a methodology to determine pavement performance incorporating maintenance and rehabilitation history using the LTPP database and ANN modeling approach. The models will be developed using data collected from the LTPP database for jointed plain concrete pavement (JPCP) from the wet, non-freeze climatic region. The M&R history is denoted as construction number (CN) in the LTPP database. The hypothesis testing demonstrated M&R treatment has a significant effect on pavement performance. Several models will be attempted to evaluate the best way to include M&R history by changing the CN variable from the LTPP database. The use of M&R history in pavement performance modeling reflects more realistic pavement conditions in the model development process. The developed models will establish better accuracy in the prediction of future pavement conditions. This can be beneficial to the policymaker for short-term and long-term budget allocation in the M&R treatment of highway pavements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".