Impact of seasonally changing variables on structural capacity and functional property of pavement
Bibliographic record
Abstract
The study is performed to identify the most important variables that impact the structural and functional properties of pavements in four climatic zones of the US and Canada utilizing data from the Long-Term Pavement Performance database. In addition to the LTTP’s Seasonal Monitoring Program (SMP) data, this study used the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data. MERRA is created by the National Aeronautics and Space Administration (NASA) for its research needs. Random forest model was used to identify variables with the most impact combined with a multiple linear regression main effect model to quantify the impact of the most important variables. Resilient modulus of the pavement top layer and the International Roughness Index (IRI) of the pavement surface were used for structural and functional properties respectively. For asphalt pavement resilient modulus, the critical climatic factors were different in the four climatic zones and the temperature related variables were found to be the most important. For rigid pavements, the variation was low for different climatic zone and also the impact of climatic variables was found not to be as significant. For asphalt pavement IRI, 80% of the important variables were the same for all climatic zones and also apart from soil temperature layer 6 from the MERRA data, the impact of other climatic variables was reduced. For rigid pavement IRI, there was only one analysis for all zones due to lack of data. Thus, the impact of zonal variance cannot be explained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".