Field inspection and laboratory testing of highway pavement rutting
Bibliographic record
Abstract
The main objective of this research was to investigate the contribution of pavement characteristics, traffic, and physical and mechanical properties of asphaltic mixtures to highway pavement rutting. A total of 51 pavement sections from the rural highway network in Jordan were selected for a case study. The average rut depths for these sections were measured and three cores were drilled for comprehensive laboratory testing. The investigation was performed using four approaches. The first approach considered pavement characteristics represented by surface thickness, last overlay thickness, pavement age, and subgrade California bearing ratio. The average annual equivalent single axle load was also included in this approach. The second approach included Marshall test parameters such as stability, flow, stiffness, and Marshall modulus. The third approach dealt with the effect of mixture air voids on rutting. The variables examined in this approach include air void content within the ruts, voids between ruts, voids near the pavement centerline, and the difference between centerline and rut voids. The fourth approach considered the dynamic permanent deformation characteristics of the pavement surface layer represented by the dynamic modulus. Regression analysis techniques were employed to develop statistical relationships between average rut depths and the parameters examined in each individual approach. The combined effect of these significant parameters on pavement rutting was also examined for prediction purposes. Rutting formation was found to be most dependent on the traffic loading, dynamic modulus of the bituminous mixture and its susceptibility to further compaction, and foundation soil strength.Key words: pavement rutting and characteristics, Marshall test, traffic loading, air voids, static creep, dynamic permanent deformation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".