Interlayer Working Conditions Classification and Treatment Measures of Airport Asphalt Pavement Overlay
Bibliographic record
Abstract
The bonding strength between the overlays of an airport asphalt pavement directly affects its service life. In this study, a finite element model of asphalt overlay on airport pavement of Juba Airport in South Sudan was established, and the elastic modulus of overlay, thickness of overlay, running state of aircraft, variation trend of interlayer shear stress under the action of load and temperature were obtained. The AHP-entropy method was used to calculate the combined weight of each influencing factor. The climate in hot and humid areas was further zoned, and a comprehensive classification of airport asphalt pavement overlay between hot and humid tropics was obtained. Four interlayer treatment measures (asphalt precoating, two oils and one aggregate, geotextile, and geogrid) and three interlayer materials (SBS-modified asphalt, ordinary hot asphalt, and SBS-modified emulsified asphalt) were designed and tested. Through shear test, wheel-load fatigue test, and pressurized seepage test, the variation trends of interlayer shear, crack resistance, and waterproofing under different combination schemes were obtained. Finally, the matching relationship between different working conditions and interlayer treatment measures was established. The results show that the actual working conditions of an airport asphalt pavement can be divided into three levels. Combined with the performance ranking of different materials and interlayer treatment measures, the recommended interlayer treatment measures for comprehensive classifications I, II, and III are two oils and one material + SBS-modified asphalt, asphalt precoating, or geogrid + SBS-modified emulsified asphalt or ordinary hot asphalt, and geotextile + ordinary hot asphalt, respectively. The working conditions in Juba Airport in South Sudan can be classified as comprehensive classification II, the recommended treatment measures for geogrid + ordinary hot asphalt.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".